Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders

Abstract

Background

Hair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are limited by subjectivity and accessibility, necessitating more advanced and accessible diagnostic tools. Artificial intelligence (AI) and deep learning offer a promising solution for more accurate and efficient diagnosis.

Methods

The research employs a modified Xception model incorporating ReLU activation, dense layers, global average pooling, regularization and dropout layers. This deep learning approach is evaluated against existing models like VGG19, Inception, ResNet, and DenseNet for its efficacy in accurately diagnosing various hair and scalp disorders.

Results

The model achieved a 92% accuracy rate, significantly outperforming the comparative models, with accuracies ranging from 50% to 80%. Explainable AI techniques like Gradient‐weighted Class Activation Mapping (Grad‐CAM) and Saliency Map provided deeper insights into the model's decision‐making process.

Conclusion

This study emphasizes the potential of AI in dermatology, particularly in accurately diagnosing hair and scalp disorders. The superior accuracy and interpretability of the model represents a significant advancement in dermatological diagnostics, promising more reliable and accessible diagnostic methods.

Abbreviations

AI

artificial intelligence

AUC

area under curve

CNN

convolutional neural network

GPU

graphics processing unit

Grad‐CAM

gradient‐weighted class activation mapping

R‐CNN

region‐based convolutional neural network

ReLU

rectified linear unit

ROC

receiver operating characteristic

XAI

explainable AI

1. INTRODUCTION

Hair and scalp disorders encompass a wide range of conditions, from common issues like alopecia and dandruff to more severe diseases such as psoriasis and folliculitis decalvans. ¹ These conditions not only affect the physical health of individuals but also have profound psychological and social implications. The distress associated with hair loss or visible scalp disorders can lead to decreased self‐esteem, anxiety, and in severe cases, social withdrawal or depression. The complexity of these disorders, often presenting with overlapping symptoms, causes a significant challenge in dermatology. ² Traditionally, the diagnosis of hair and scalp disorders has relied heavily on clinical examination, patient history, and a range of diagnostic tools. ³ Dermoscopy, for instance, has been a staple in dermatological diagnostics, offering a non‐invasive method to examine the microstructures of the scalp and hair. ⁴ However, the interpretation of dermoscopic images is highly subjective and depends greatly on the clinician's experience and expertise. ⁵ Biopsy, while offering conclusive evidence, are invasive and often not the first choice in diagnostic procedures. ⁶ The subjective nature of current diagnostic methods leads to a significant variability in diagnoses. ⁷ Different conclusions may arrive when presented with the same symptoms, leading to a potential misdiagnosis. Misdiagnosis can result in ineffective or even harmful treatments, prolonging patient distress and leading to unnecessary healthcare expenditures.

Advancements in medical imaging have brought about more sophisticated tools like high‐resolution ultrasonography and magnetic resonance imaging, which provide detailed views of the hair and scalp. ⁸ However, these technologies are not without limitations. The interpretation of images remains subjective, and the high cost and limited availability of advanced imaging tools restrict their widespread use. ⁹ Furthermore, there is a notable scarcity of comprehensive datasets, especially for rare or complex disorders, which hampers the development of more advanced diagnostic tools.

Recent years have shown tremendous potential for deep learning and other forms of artificial intelligence (AI) to revolutionize medical diagnosis. ¹⁰ Deep learning algorithms, trained on large datasets of medical images, have demonstrated remarkable accuracy in diagnosing various conditions, from skin cancers to retinal diseases. ¹¹ Which means in dermatology, AI has the potential to analyze complex patterns in skin and scalp images, offering objective and consistent diagnoses. ¹² Especially deep neural transfer learning, involves applying knowledge gained while solving one problem to a different but related problem. For dermatology, this means using neural networks pre‐trained on vast datasets (not necessarily medical) and fine‐tuning them for specific tasks like diagnosing hair and scalp disorders. ¹³ This approach is particularly advantageous in situations where medical data is scarce or when rare conditions are involved. ¹⁴ Transfer learning can mitigate the data scarcity issue also by leveraging pre‐existing models, thus enhancing the precision of diagnoses. The application of deep neural transfer learning in diagnosing hair and scalp disorders could revolutionize dermatological diagnostics. ¹³ By providing more standardized, objective, and accurate diagnoses, AI can reduce the rate of misdiagnoses, lead to more effective treatment plans, and improve patient outcomes. Furthermore, AI can assist in early detection of disorders, which is crucial for conditions where early intervention can prevent severe progression. ¹⁵

Overall, the difficulty of diagnosing hair illnesses highlights the need for significant advances in testing accurateness. The issues with present solutions only heighten the need of finding alternative approaches. The advent of deep neural transfer learning might revolutionize the world. It has the potential to improve patient outcomes by standardizing and improving the accuracy of diagnosing hair and head illnesses. Because of these issues, we combined two sources to create a new dataset, classifying cases of hair and scalp disorders such alopecia, dissecting cellulitis, effluvium, folliculitis, psoriasis, and acne keloidalis nuchae into six categories. This will make it feasible to find out about a disease even while it is still in its early stages. Finally, this research aims to demonstrate how to detect many hair‐and scalp‐related disorders using a specialized deep learning model built on Xception. The effectiveness was shown by comparing accuracy vectors and using transparent AI which are:

1. Development of an Enhanced Xception‐based model: Our research significantly contributes to the field of dermatological AI by developing an advanced variant of the Xception model. This model is specifically tailored for the diagnosis of hair and scalp disorders, incorporating modifications like ReLU activation functions, dense layers, global average pooling (GAP), and dropout layers. This adaptation not only improves accuracy but also makes the model more suitable for the specific complexities of dermatological imaging.

2. High diagnostic accuracy and precision: A major contribution of our work is achieving an unprecedented accuracy rate of 92% in diagnosing hair and scalp disorders. This level of precision surpasses previous models and methods, marking a significant advancement in the field.

3. Application of explainable AI (XAI) techniques: Our research integrates XAI techniques, such as Grad‐CAM and Saliency Map, into the diagnostic process. This approach enhances the transparency and interpretability of our research outcomes in medical diagnostics, fostering trust among clinicians and patients.

4. Comprehensive comparative analysis with existing models: Our work includes a thorough comparative analysis of our model against existing models like VGG19, Inception, ResNet, DenseNet, and so forth. This comprehensive evaluation not only demonstrates the superiority of our approach but also provides valuable insights into the strengths and weaknesses of various models in the context of diagnosing hair and scalp disorders.

5. Potential for early detection and improved patient outcomes: The early detection capabilities of our model represent a major contribution to patient care. By accurately diagnosing conditions in their early stages, our model can lead to more effective treatment strategies, reducing the emotional and physical burden on patients and potentially lowering healthcare costs.

There is a total of six sections broken down as follows inside the body of the paper: Separated into an introductory section and a section reviewing previously published works under first two sections. In Section 3, we will discuss the methodology; in Section 4, we will examine the outcomes and make some comparisons. The article's discussion, conclusion and future research directions are discussed in Section 5 and 6.

2. LITERATURE REVIEW

2.1. Background study

Hair is not only a critical aspect of physical appearance but also serves as an indicator for overall health. ¹⁶ Both men and women take immense pride in their hair, making hair‐related issues a source of significant concern for many. Hair loss, in particular, is not just a cosmetic problem but can be an early indicator of deeper health problems or congenital anomalies. ¹⁷ This connection between hair health and general well‐being necessitates a deeper understanding and research. Globally, an increasing number of individuals are battling with hair disorders, ranging from alopecia to scalp diseases, and the trend is particularly noticeable among women. ¹⁸ The rise in hair problems can be attributed to various factors, including environmental pollution, lifestyle changes, stress, and genetic predispositions. ¹⁹ The growing prevalence underscores the urgent need for effective solutions and treatments. The pursuit of research in hair diseases and disorders has been ongoing, yet there remains a substantial gap in the understanding and management of these conditions. Traditionally, research has concentrated on certain elements such as the causes of hair loss, the emotional consequences of hair problems, and the effectiveness of different treatment methods. Nevertheless, there is still a need for a multidisciplinary approach that considers the complex character of hair problems.

2.2. Hair diseases

According to previous research, noticeable amount of people in the United States have hair and skin problems that are not being handled properly. ²⁰ Dermatologists need to undertake both physical and ocular examinations, which takes time, to detect hair‐related disorders. When diagnosis is postponed, the condition just worsens. In healthcare and health computing, images and applications powered by neural networks are used to diagnose potentially fatal conditions like cancer and tumors. This research immersed deep learning to predict whether or not a person will have baldness, psoriasis, or folliculitis. Due to the small sample size and lack of a suitable image collection, they were forced to compile 150 images from various sources. They were able to attain 91.1% accuracy in the test phase after entering the processed data into the convolutional neural network (CNN) model. Therefore, it was clear from the data alone that they could not rely on the model. An effective method of testing and diagnosing the effects of scalp hair massage is proposed in a distinct article. ²¹ This system, known as ScalpEye, uses deep learning, and it has been the subject of much discussion. With the use of the ScalpEye technology, a variety of hair and scalp issues may be located and identified. Dandruff, folliculitis, hair loss, and unclean hair are all examples of these. The ScalpEye system examines the scalp and hair to identify any issues via the use of image recognition. After extensive testing of many well‐known models, the authors settled on the quicker Inception ResNetV2‐Atrous model for object recognition. After evaluating many other widespread models for object recognition, they settled on this one. The ScalpEye method was shown to be very accurate in diagnosing four common hair and scalp issues. It has an average accuracy of 99.09%, with a range of 97.41% to 99.09%.

An effective and practical algorithm was developed for recognizing hair cells and measuring hair loss with the use of a mixed deep learning approach embedded inside a framework based on a Mask region‐based convolutional neural network (R‐CNN). ²² Hair loss severity was measured by counting hairs, measuring hair breadth, and counting the quantity of hairs. Ten males were photos at varying levels of hair loss, and using these criteria, the hair cells were classified as healthy, normal, or severe. When compared to other approaches, R‐CNN's classification accuracy was 4%–15% higher. Given its efficacy, it has the potential to improve hair loss diagnosis and treatment in clinical settings. They were too concerned about hair loss, which is rather minor compared to other health problems. In a separate study, the authors developed a framework ²³ to demonstrate how to distinguish between alopecia areata and normal hair. During the phase of producing the photos, they utilized 68 photos of alopecia areata‐affected hair from the Dermnet dataset and 200 pictures of healthy hair from the Figaro1k collection. S.V.M. and K.N.N. achieved 91.4% and 88.1% accuracy, respectively, in a 10‐fold cross‐validation test. Which, given the limited sample size, is not very reliable.

Deep learning and XAI techniques were employed in this research using images of hair and the human head. ²⁴ It was 63.9% accurate in putting photos of the head from the DermNet collection into groups for acne keloidalis, baldness, and other disorders. Both the DermNet and Figaro1k image databases have 100% of their images accurately classified as either excellent or bad. Visual explanations for the decisions made by the deep models were obtained by applying Gradient‐weighted Class Activation Mapping (Grad‐CAM), Local Interpretable Model‐Agnostic Explanations (LIME), and occlusion sensitivity on the test images alongside the model predictions. This research only utilized information available online; no physicians or other specialists were contacted. As a consequence, the available data and expertise governed how well XAI findings could be categorized and analyzed. The research would benefit from a larger and more representative sample. This would improve the trustworthiness of the trained model and address the issue of models fitting too well. The accuracy of hair density measurement (HDM) was investigated in this study utilizing deep learning to locate objects, and it was discovered that HDM could be performed automatically. ²⁵ One thousand four hundred Red, Blue, and Green photos of balding men's scalps were utilized for training and testing. Images of hair follicles and the information about their location and kind depending on the quantity of hairs shown were provided. The accuracy of the object detection tools EfficientDet, YOLOv4, and DetectoRS were compared. YOLOv4 performed the best in the tests, with an average accuracy of 58.67%.

Daniels et al. ²⁶ implemented machine learning on a dataset of images depicting virgin and blonde hair before and after being treated with a shampoo and conditioner combination designed to add volume, smooth the hair cuticle, and prevent flyaways. The hair alignment, local, and global hair volumes, and hair volumes were automatically measured from the hair pictures. These characteristics were evaluated at time points t0 (before any therapy), t1 (after two treatments), and t2 (after three treatments). Classifier tests were employed to evaluate the performance of the machine learning. Classification tests performed well when hair images were separated into groups representing before and after treatment, revealing differences in hair's volume and direction. Automatic image analysis was backed up by human appraisal of hair that was exhibited in pairs. They created a database of 288 phototrichogram images, meticulously tagged with the position and length of each hair, for a similar deep learning‐based investigation. ²⁷ We created our own neural network architecture and image processing algorithms to get the most out of the training data we had access to and to achieve the best results for measuring hair length and counting hairs. We validated the accuracy of the algorithm by comparing the measured hair counts and lengths to those obtained using Canfield's Hair Metrix, a semi‐manual ground truth approach. Utilizing deep neural networks and ML, we were able to reduce the time it took to get data from phototrichograms of patients’ scalps from months to seconds. Our software allows for the rapid and automated evaluation of hair length and density. The algorithm is in close agreement with human‐assisted research (the real thing).

In this research, the authors propose a novel hair loss detection network. Damage to hair surfaces seen under a scanning electron microscope (SEM) was analyzed using AI techniques, which automatically detected and categorized the damage. ²⁸ Simultaneously, they used SEM microscopy pictures to create a new data set on hair thinning. Damage to the hair was classified into mild, moderate, and severe categories using mathematical analysis of SEM (scanning electron microscopy) image data to create a new hair microscope data set. They developed residual channel spatial attention network (RCSAN‐Net), a novel and effective convolutional network model for detecting baldness. It was absolutely precise. Using deep learning and image processing, the authors of this work propose a technique for quantifying hair density. ²⁹ The proposed setup consists of a smartphone, a Cloud Work Space platform in the cloud, and a Cloud AI machine for processing images in the cloud. Finding the regions with hairy pores (i.e., hair cell pores with hairs), counting the number of hair shafts in these regions, and calculating the ratio of thick to thin hair shafts is how the proposed approach may also determine how much hair is on a hairy head image. The results of the tests show that the proposed approach can reliably locate hairy pore regions and estimate hair volume within 95% of the time. In this paper, the authors suggest a technique for self‐diagnosing scalp issues and halting hair loss using images captured by a camera coupled to a smart smartphone. ³⁰ Combining grid line selection and eigenvalue with a microscope image of the head yields the hair loss factor (HLF). To begin, we adjust the brightness of the microscope data and lessen the amount of light reflection in the captured head images by using image processing. Second, HLF is retrieved using a different way to figure out how much hair loss has transpired based on an image of the head that has already been processed. We characterize HLF as the total quantity of hairs, hair follicles, and hair thickness, taking into consideration damaged hairs, short vellus hairs, and hairs that become thinner as they develop.

In another work, ³¹ researchers demonstrated a novel method for instantaneously calculating a patient's hair density and thickness using a smartphone and digital image analysis. Sending trichoscopy images from a smartphone equipped with a dermatoscope lens to a computer for analysis is now possible. Compared to the 9.2 min required for hand trichometry, this approach only required 24 s of the clinician's time. Which may be quickly used in clinical settings to enhance standard trichoscopy. In order to improve the accuracy of the hair identification algorithm utilized in this research, the authors advised using AI in the future. For instance, AI might aid in locating hairs of a more diverse color palette or in less clear photos. Patients might be located by the use of hair dye in the scanning region. According to yet another in‐depth research, the applications of deep learning for hair restoration are constantly evolving. If there are more accurate methods of measuring hair growth, it will be simpler to identify and treat hair disorders. ³² In order to be effective for diagnosis, deep learning libraries for identifying scalp dermoscopy images will need extensive labelling by hair specialists. The decision of how to treat hair loss might be aided by new hair loss prediction technologies. People who work in the field of hair regeneration should be aware of the benefits and drawbacks of these new technologies, which will be useful for both physicians and patients.

2.3. Other skin diseases

An assessment model ³³ classifies the patients into four groups. The system relies on five distinct types of machine learning techniques, a metaheuristic optimization technique, and pre‐trained models of CNNs. It is named Harris Hawks Optimizer (HHO) and it is used to fine tune the hyperparameters of five models that have previously been learnt. VGG19, VGG16, Xception, MobileNet, and MobileNetV2 are the models in question. After that, the layers that are prepared to have their attributes extracted may be selected. Seven different machine learning models, including K‐Nearest Neighbors, Random Forest, AdaBoost, Histogram Gradient Boosting, Support Vector Machine, and Extra Trees, are used to classify these data into categories. All figures shown are weighted averages. The outcomes of the majority vote are then analyzed using machine learning techniques and the trained model. The positive predictive value (PPV) is 97.96%, the sensitivity is 95.11%, the specificity is 95.11%, the intersection over union is 96.58%, the receiver operating characteristic (ROC) is 96.65%, and the accuracy is 97.53%. Accuracy in the MPID dataset is 97.51%, sensitivity is 94.48%, specificity is 94.88%, PPV is 94.96%, F1 is 96.66%, and ROC is 96.69%. Despite the model's success with both data sets, researchers were worried about the arithmetic mean accuracy that would result from assigning greater weight to the class with the most occurrences. However, a limited number of shots were used to evaluate the model. There were many types of disease, but only a handful were clearly distinguishable from one another. We need more robust models to differentiate between the various diseases that have similar symptoms. In this research, an AI system was developed to analyse skin scans for the presence of illness. ³⁴ The skin photos originate from a public collection of photos. Following these procedures, state‐of‐the‐art deep learning models were employed to detect symptoms of disease. These models included CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG, SE‐ResNet, and Xception. To improve upon our previous classification model, we combined the LSTM model with the top two deep learning models. The mixed AI system was tested for detecting skin illnesses, and it was found to be successful 87% of the time. Cohen's kappa was 0.8222 for this study. Due to the authors’ attempts to replicate the format of prior research, distinguishing between the three illnesses is challenging. However, the same research was repeated several times, each time focusing on the same three illnesses. In order to classify patients with skin disease symptoms with those with chickenpox, measles, and normal symptoms, the VGG16, VGG19, ResNet50, ResNet101, DenseNet201, and AlexNet models ³⁵ were applied. Preliminary computational testing demonstrates that the proposed model successfully identifies individuals with skin disorders. As of today, ResNet101 has an area under curve (AUC) of 98.59% and a total accuracy of 94.25%. It is also spoken about how to categorize the model by extracting features using LIME. This data can help us figure out what makes the monkeypox virus so special. Accuracy was high since there were just three groups and a limited dataset. Other persons, however, were able to achieve higher precision using the same images. In this study, we aim to evaluate deep learning models for their ability to detect Monkeypox. ³⁶ The characteristics of the images were extracted using two different CNN models, namely, GoogLeNet and ResNet50. ResNet50, VGG‐16, SqueezeNet, and InceptionV3 were used for this. The findings demonstrated that all approaches performed similarly well. The best model was VGG‐16 (accuracy = 0.96, F1‐score = 0.92). The authors of the study recommend doing further research on the models and creating larger image sets in order to improve the reliability of evaluations.

Here, deep learning is immersed to determine whether or not moles on the skin are malignant. ³⁷ To determine which deep learning model was the most sensitive, accurate, and exact, many models were pitted against one another. It is possible that many individuals will use the effective method proposed for detecting skin illnesses. They were mostly correct, although their use of binary categorization was limited by a lack of data. This is not the ideal technique to deal with delicate issues like hazardous illnesses. In this study, monkeypox was identified among cowpox, smallpox, and chickenpox using animal models VGG‐19, VGG 16, MobileNet V2, GoogLeNet, and EfficientNet‐B0. ³⁸ The quality of MobileNet V2's data‐sorting in the updated dataset earned it a score of 99.25%. Out of all the methods tested, this one performed the best with both the original and modified data sets. In contrast, VGG‐19 correctly predicted 78.82% of the data. These results suggest that the shallow model is more effective with fewer image datasets. As more data was collected, the success rate of deep networks increased. This is so due to the fact that optimal weights for deep models might be adjusted. The “Monkeypox Skin Images Dataset,” or “MSID”, ³⁹ was created specifically for the sake of this investigation. Their new deep learning‐based CNN model is dubbed MonkeyNet. They performed some preliminary testing of it. This research recommended utilizing a deep CNN to detect skin illnesses, with a 93.19% success rate on the original dataset and a 98.91% success rate on the enhanced dataset. The weighted accuracy and the Grad‐CAM are both shown in this version. The Grad‐CAM evaluates the efficacy of the model and points out the locations in each image where medical attention is needed. Though it was impressively precise, it may be even more useful if it could distinguish between harmless skin issues and more dangerous conditions that share symptoms with smallpox.

To top it all off, most hair research is concentrated on limited regions by disregarding crucial demands. Where there were voids, they placed hair loss or hair problems like hair thickness at the top of the list, even though most individuals avoid significant hair or head disorders like alopecia areata, folliculitis, and effluvium. Some researchers attempted to study hair infections, but they were only interested in a binary taxonomy that is not really useful when there are so many possible ailments. However, several investigations into skin disorders have made use of medical imaging techniques. In particular, after reviewing the literature on the subject of early diagnosis of hair and skin disorders, our team concluded that deep learning methods such as Xception, Inception, DenseNet, R‐CNN, ResNet, and others may be useful for classifying hair diseases into early stages. We need to work out not just which combination will provide the optimum outcome, but also how to make our findings more transparent via more in‐depth approaches.

3. MATERIALS AND METHODS

3.1. Dataset overview

In this research, we utilized a meticulously curated dataset, a confluence of two distinct dermatological datasets. This amalgamation was aimed at creating a comprehensive and diverse dataset, crucial for the robust training and evaluation of our model.

The primary dataset in our study is the Dermnet dataset, ⁴⁰ a well‐established resource in dermatological research. Dermnet is renowned for its extensive visual data encompassing a wide spectrum of skin conditions. For our specific focus on scalp and hair diseases, we extracted relevant data from Dermnet, encompassing conditions such as acne keloidalis nuchae, alopecia, dissecting cellulitis, effluvium, folliculitis, psoriasis.

Complementing the Dermnet dataset, we incorporated data from a previous study, referred to as Scalp‐Hair‐Diseases‐Detection, ²⁰ which primarily included cases of alopecia, folliculitis, and psoriasis. This dataset, though more limited in scope, provided valuable additional samples for our target conditions. The combined dataset comprises a total of 241 images, distributed across various conditions as detailed in Table 1. This table outlines the specific conditions and the number of samples from each source dataset. To further elucidate the distribution of conditions within each dataset, we provide two additional tables: Tables 2 and  3, representing the Scalp‐Hair‐Diseases‐Detection and a partial selection from Dermnet, respectively.

TABLE 1.

Titles, groups, and picture information were gathered for the collection.

Dataset	Conditions	Samples
Dermnet 40	Acne keloidalis nuchae, alopecia, dissecting cellulitis, effluvium, folliculitis and psoriasis	156
Scalp‐hair‐diseases‐detection 20	Alopecia, folliculitis, and psoriasis	85

TABLE 2.

Scalp hair diseases detection ‐ Data gathered for the collection used in ref. [20].

Scalp hair diseases detection
Alopecia	45
Folliculitis	15
Psoriasis	25

TABLE 3.

DermNet (partial) ‐ Collected portion of the well‐ recognized dataset named DermNet40.

Dermnet (Partial)
Acne Keloidalis Nuchae	49
Alopecia	63
Dissecting cellulitis	10
Effluvium	11
Folliculitis	23

In the process of preparing the dataset for model training, validation, and testing, we initially partitioned the data into two primary segments: 85% for training and 15% for testing. To further enhance the model's ability to generalize, we subdivided the training segment into two subsets: approximately 70% of the total dataset for training and an additional 15% for validation purposes. This strategic division ensures a robust training process while maintaining a representative subset for both validation and testing. Table 4 illustrates this distribution, detailing the allocation of samples across each specific condition for training, validation, and testing phases.

TABLE 4.

Merged both of the datasets to train, validate, and test with a more enriched one.

Scalp and hair diseases merged dataset
	Dataset split
	Train (85%)
Disease	Train (70%)	Validation (15%)	Test (15%)	Total
Acne keloidalis nuchae	36	6	7	49
Alopecia	78	14	16	108
dissecting cellulitis	7	1	2	10
effluvium	7	2	2	11
folliculitis	28	5	5	38
psoriasis	18	3	4	25

Our approach employed stratified sampling to ensure proportional representation of each class in both the training and validation sets, addressing potential class imbalance issues typical in medical datasets. The final dataset preparation, including the meticulous division into training, validation, and testing sets, lays a solid foundation for developing a reliable and effective model for hair disease classification. A few samples for each class are depicted in Figure 1.

FIGURE 1.

Sample images of each of the classes from the merged dataset: (A) acne keloidalis, (B) alopecia areata, (C) dissecting cellulitis, (D) effluvium, (E) folliculitis, (F) psoriasis.

3.2. Experimental setup

3.2.1. Dataset loading and processing

For efficient handling and processing of the dataset, we employed a structured approach using Keras functionalities, which included the following steps:

1. Training dataset: The training dataset is augmented to enhance the model's ability to generalize. This augmentation includes transformations such as random shifts, rotations, zoom, shear, and flips. The data is batched with a size of $16$ images, and shuffling is applied to introduce randomness and prevent biases. This approach helps in simulating a more diverse set of training scenarios, essential for robust learning.

2. Validation dataset: Similar to the training dataset, the validation data is batched with a size of $16$ images but without any augmentation. This approach ensures a more realistic evaluation of the model's performance during training. The validation data is not shuffled to maintain the consistency of the evaluation process.

3. Test dataset: The test dataset is loaded without any augmentation and shuffling. This is critical to maintain the integrity and consistency of the evaluation process, as it represents the model's performance in real‐world conditions. The test data is also batched with a size of $16$ images for efficient processing.

This structured methodology ensures that each dataset segment is optimally prepared to serve its purpose in the model development process, from training and validation to final testing.

3.2.2. Data preprocessing techniques

Data preprocessing forms the cornerstone of our methodology, priming the raw image data for effective ingestion into the neural network. Key steps in our preprocessing pipeline include:

1. Resizing: Each image in our dataset is resized to a consistent dimension of $224\times 224$ pixels, ensuring uniformity in input size.

2. Normalization: Pixel values of each image, originally in the range of $0$ to $255$, are scaled down by a factor of $255$. This step transforms the pixel value range to $[0,1]$, facilitating a smoother optimization process during training.

3.2.3. Augmentation strategies

To enhance the diversity of our training dataset and to improve the model's ability to generalize, we implement the following data augmentation techniques:

1. Spatial shifts: Random translations are applied both horizontally and vertically with a range up to $10\%$ of the total width or height of the image.

2. Rotational dynamics: Images are randomly rotated within a range of $\pm 2$ degrees.

3. Zoom variations: Random zoom in and zoom out operations are applied, with a range up to $10\%$.

4. Shear transformations: Random shearing transformations are applied within a range of $\pm 10\%$.

5. Brightness variation: The brightness of images is varied within a range of $80\%$ to $100\%$ of the original brightness.

6. Flip operations: Horizontal flips are performed to create mirrored images.

7. Fill mode: The “nearest” fill mode is used for newly introduced pixels after a spatial transformation.

3.2.4. Model architecture: Selection of base model

We opted for the Xception architecture, a deep learning model renowned for its ability to capture intricate features in images. The Xception model is characterized by its depth‐wise separable convolutions, which significantly reduce computational complexity while maintaining high representational capacity. ⁴¹

3.2.5. Model architecture: image preprocessing

Prior to input, images were resized to a standardized 224 × 224 pixel format, ensuring uniformity in input size. This is denoted as:

$$\mathrm{Input}\mathrm{Image}\mathrm{Size}=224\times 224\mathrm{pixels}$$ (1)

3.2.6. Model architecture: Transfer learning with Xception

Our model depicted in Figure 2 which harnesses the powerful technique of transfer learning by incorporating the Xception architecture, which has been pre‐trained on the extensive ImageNet dataset. This strategy allows the model to benefit from a rich feature‐recognition capability developed through exposure to a vast array of image data. The Xception network, known for its depthwise separable convolutions, provides an advanced starting point for learning, significantly reducing the training time and computational resources required to achieve high levels of accuracy.

FIGURE 2.

Xception‐based multistage model architecture with six classes.

3.2.7. Model architecture: Feature extraction with Xception

The convolutional base of the Xception model, renowned for its efficiency and accuracy, is employed within our architecture to serve as a feature extractor. This component of the model is adept at discerning various levels of abstractions within the image data. Through the use of depthwise separable convolutions, it captures essential attributes ranging from basic visual elements such as edges and textures to progressively intricate patterns, which are fundamental in constructing a nuanced understanding of the visual content. These extracted features form the foundational inputs for the subsequent layers of the network, which are responsible for the higher‐level reasoning and classification tasks.

3.2.8. Model architecture: GAP

After feature extraction, GAP was applied to the feature maps. This operation computes the spatial average of each feature map, resulting in a condensed representation. The GAP layer is defined as:

$$GAP\left(x_{ij}\right)=\frac{1}{H\times W}\sum _{i=1}^H\sum _{j=1}^W{x}_{ij}$$ (2)

where $x_{ij}$ represents the element at row $i$ and column $j$ of the feature map, and $H$ and $W$ denote the height and width of the feature map, respectively.

3.2.9. Model architecture: Batch normalization

Batch normalization layers were introduced to stabilize and accelerate the training process. This technique normalizes the activations of neurons within a batch, reducing internal covariate shifts. The batch normalization operation is defined as:

$$\mathrm{BN}\left(x\right)=\gamma ·\frac{x-\mu }{\sqrt{{\sigma }^2+\epsilon }}+\beta$$ (3)

where $x$ is the input, $\mu$ and $\sigma$ are the mean and standard deviation, $\gamma$ and $\beta$ are learnable scale and shift parameters, and $\epsilon$ is a small constant to prevent division by zero.

3.2.10. Model architecture: Dense layers with ReLU activation and L2 regularization

In addition to the ReLU activation functions, each dense layer incorporates L2 regularization. L2 regularization, also known as weight decay, imposes a penalty on the magnitude of weights in the network. This helps prevent overfitting by discouraging overly complex models and encouraging simpler ones. The L2 regularization term is defined as:

$$L2\_Regularization=\lambda \sum _i{w}_i^2$$ (4)

where $w_i$ represents the weights of the model and $\lambda$ controls the strength of the regularization.We have used the value is $1\times {10}^{-3}$

The ReLU activation function is defined as:

$$\mathrm{ReLU}\left(x\right)=\max \left(0,x\right)$$ (5)

ReLU introduces nonlinearity, enabling the model to learn complex relationships within the data.

3.2.11. Model architecture: Dropout regularization

Overfitting is a prevalent challenge in complex neural network architectures, especially when dealing with limited training data. To address this, our model incorporates dropout regularization, a technique designed to enhance the network's ability to generalize by reducing over‐reliance on any individual neuron. Dropout works by probabilistically deactivating a subset of neurons during the training phase, which can be mathematically represented as follows:

(6)

Here, $\mathbf{h}$ is the vector of activations from the previous layer, $\mathbf{d}$ is a binary vector where each element is drawn from a Bernoulli distribution with probability $p$, and $\odot$ denotes element‐wise multiplication. The resulting vector ${\mathbf{h}}^{\prime }$ is then passed to the subsequent layer during the forward pass. By dropping a fraction of the activations, the network is prevented from becoming too reliant on specific paths, potentially improving its robustness and performance on unseen data.

The probability of retaining a neuron during training, denoted by $p$, is a hyperparameter that is, typically chosen through cross‐validation. The dropout rate, $1-p$, is often set between 0.2 and 0.5; higher rates may lead to underfitting, while lower rates may be insufficient to prevent overfitting.

When the network is in inference mode, dropout is deactivated to utilize the full capacity of the model. This is mathematically equivalent to scaling the activations by $p$, ensuring that the expected sum remains unchanged from the training phase:

(7)

In our model's architecture, dropout layers are strategically placed after each dense layer except the final output layer, which is tasked with producing the probability distribution across the multiple classes. This approach to regularization is especially beneficial when the training dataset is not sufficiently large, as it encourages the development of a more distributed and robust feature representation.

3.2.12. Model architecture: Final prediction layer

The culmination of our neural network architecture is the final dense layer, which serves as the decision‐making entity of the model. This layer utilizes the softmax activation function, a critical component for multiclass classification problems. The softmax function is mathematically represented as:

$$\sigma {\left(\mathbf{z}\right)}_i=\frac{e^{z_i}}{{\sum }_{j=1}^K{e}^{z_j}}$$ (8)

where $i$ corresponds to the $i$‐th element of the output vector $\mathbf{z}$, $K$ is the total number of classes, and $\sigma {(\mathbf{z})}_i$ is the probability that the input belongs to class $i$. Essentially, the softmax function exponentiates each output score (logit) $z_i$ and normalizes these values over all classes, yielding a probability distribution where the probabilities sum to one.

This layer's outputs are probabilities that sum up to one across all classes, providing a quantifiable confidence level for each class prediction. It is particularly adept at handling cases where the class membership is mutually exclusive, ensuring that our model can estimate with high fidelity the likelihood of an input image belonging to each specific category it has been trained to recognize.

3.2.13. Model architecture: Summary

In summary, our model architecture amalgamates cutting‐edge practices and innovative techniques in the realm of deep learning. The key features and advantages of our model architecture are outlined below:

1. Base model: Leveraging the robust Xception architecture as our base model establishes a solid foundation. This model is pretrained on ImageNet, harnessing extensive knowledge from a vast dataset.

2. Multistage design: The incorporation of a multistage design, encompassing GAP, batch normalization, and dense layers, serves to extract intricate hierarchical features from input data. This design enhances both performance and computational efficiency.

3. Regularization strategies: To mitigate overfitting and coadaptation, our model employs dropout and batch normalization within dense layers. These regularization strategies contribute to the model's resilience and generalization capabilities.

4. Softmax activation: The final prediction layer utilizes a softmax activation function. This transformation converts raw logits into class probabilities, facilitating confident and meaningful classification.

Subsequent sections will present comprehensive results and analyses, showcasing the efficacy and superiority of our model architecture across diverse image classification tasks.

3.2.14. Explaible AI: Methodological overview

This section explores the theoretical framework of applying deep learning techniques for dermatological image analysis, emphasizing the use of TensorFlow and Keras. The methodology includes several critical components:

1. Deep learning frameworks: TensorFlow and Keras are utilized for constructing and implementing deep learning models, providing essential tools for complex computations and neural network layers.

2. Image processing techniques: Dermatological images are standardized in size and undergo normalization and noise addition, enhancing model robustness and data variability.

3. Adaptation of pre‐trained models: Leveraging pre‐trained models adapted for specific visualization tasks is a core strategy, often involving modifications to suit medical image analysis requirements.

4. Visualization techniques: Advanced techniques like Saliency Maps and Grad‐CAM are crucial for highlighting regions within images that significantly influence the model's predictions.

5. Heatmap generation for interpretability: Generating heatmaps using Grad‐CAM offers a visual representation of the model's focus areas, enhancing interpretability.

6. Analytical evaluation: Evaluating the model's predictions against actual labels is essential for assessing accuracy and performance in medical image analysis.

7. Comprehensive visualization of results: Detailed visualization of the original images, alongside their corresponding saliency maps and heatmaps, provides a holistic view of the model's analytical process.

3.2.15. Explaible AI: Theoretical foundations and key equations

The methodology is grounded in established deep learning principles, particularly in image classification and interpretation. Key equations in these methodologies include:

1. Grad‐CAM

Grad‐CAM, ⁴² or Gradient‐weighted Class Activation Mapping, is a technique for making CNN‐based models more transparent and interpretable. It involves using the gradients of any target concept (like a specific class in a classification network) flowing into the final convolutional layer to produce a localization map. This map highlights the important regions in the image for predicting the concept. The key equation for Grad‐CAM is:

$${\alpha }_k^c=\frac{1}{Z}\sum _i\sum _j\frac{\partial y^c}{\partial A_{ij}^k}$$ (9)

Here, ${\alpha }_k^c$ represents the neuron importance weights for class $c$, $y^c$ is the score for class $c$, $A^k$ is the feature map of a convolutional layer, and $Z$ is the number of pixels in the feature map.

• 2.Saliency Maps

Saliency Maps ⁴³ are used to visualize the most critical parts of an image for a model's decision. They are generated by computing the gradient of the class score concerning the input image. This technique highlights areas in the image that contribute most significantly to the class score. The equation for a Saliency Map is:

$$S_c\left(I\right)=\frac{\partial y^c}{\partial I}$$ (10)

In this equation, $S_c(I)$ is the saliency map for class $c$ given an input image $I$, and $y^c$ is the score for class $c$. This map effectively illustrates which parts of the input image are most influential in the classification decision.

The integration of advanced deep learning techniques in dermatological image analysis underscores the potential of AI in enhancing interpretability and efficacy in medical image analysis. Tools like Grad‐CAM and Saliency Maps provide profound insights into neural network decision‐making processes, marking significant steps in medical diagnostics.

3.2.16. Training approach

To achieve model convergence and efficiency, our training approach involved using the Adam optimizer with a learning rate of $1\times {10}^{-4}$ and categorical cross‐entropy as the loss function. The dataset, comprising both original and augmented images, was carefully managed with a batch size of 16 to ensure stability during training.

To prevent overfitting, early stopping with a patience of 3 epochs was implemented, and model checkpoints were used to retain the best‐performing model based on validation loss. The training specifications, outlined in Table 5, include various cutting‐edge models configured for optimal performance on the NVIDIA Tesla T4 graphics processing unit (GPU).

TABLE 5.

Training specifications.

Model name	Batch size	Learning rate	Optimizer	GPU
MobileNetV2	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
NASNetMobile	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
DenseNet201	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
InceptionResNetV2	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
ResNet152V2	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
NASNetLarge	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
VGG19	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
InceptionV3	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
Xception	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4
Proposed model	16	$1\times {10}^{-4}$	Adam	NVIDIA Tesla T4

Abbreviation: GPU, graphics processing unit.

Our streamlined training strategy, conducted over 100 epochs, prioritized efficiency, with the potential for early stopping. Leveraging multiple workers and the computational capabilities of the NVIDIA Tesla T4 GPU on Google Colab expedited the process, ensuring swift and resource‐efficient model optimization.

4. RESULTS

In this section, we present the outcomes of our exhaustive analysis of diverse hair disorders and the subsequent development of an innovative classification system based on the Xception model. The primary objective is to underscore the superiority of our model, achieved through a meticulous comparison with the current industry standard. Evaluation metrics, including accuracy, precision, recall, loss, AUC, and F1‐score, were employed to provide a thorough assessment.

The Xception model exhibited exceptional performance during both the validation and test stages, as detailed in Tables 6 and 7. Notably, future potential enhancements are considered, supported by indications that further refinements to the Xception model could yield even more favorable results. Subsequent improvements were implemented, elevating the model's performance to an outstanding level.

TABLE 6.

Comparative performance of CNN architectures for validation sets.

CNN method name	Accuracy	Loss	Precision	Recall	F1‐score	AUC
MobileNetV2	0.52	1.86	0.21	0.24	0.21	0.81
NASNetMobile	0.52	1.75	0.41	0.25	0.24	0.76
DenseNet201	0.55	1.22	0.30	0.29	0.28	0.87
InceptionResNetV2	0.55	2.3	0.24	0.26	0.23	0.78
ResNet152V2	0.55	1.76	0.31	0.34	0.32	0.82
NASNetLarge	0.55	1.99	0.36	0.38	0.36	0.83
VGG19	0.58	1.18	0.38	0.40	0.37	0.79
InceptionV3	0.71	0.89	0.48	0.45	0.43	0.91
Xception	0.81	0.86	0.74	0.65	0.66	0.93
Proposed model	0.90	0.66	0.88	0.88	0.85	0.95

Abbreviation: AUC, area under curve.

TABLE 7.

Comparative performance of CNN architectures for test sets.

CNN Method Name	Accuracy	Loss	Precision	Recall	F1‐score	AUC
MobileNetV2	0.5	2.45	0.25	0.23	0.21	0.73
NASNetMobile	0.5	1.55	0.34	0.23	0.22	0.83
VGG19	0.53	1.21	0.32	0.37	0.33	0.81
DenseNet201	0.56	1.18	0.38	0.33	0.31	0.90
InceptionResNetV2	0.56	2.06	0.42	0.28	0.28	0.81
NASNetLarge	0.56	2.40	0.38	0.42	0.37	0.78
ResNet152V2	0.58	1.48	0.54	0.42	0.45	0.85
InceptionV3	0.72	0.72	0.45	0.49	0.47	0.97
Xception	0.78	0.82	0.53	0.54	0.53	0.97
Proposed model	0.92	0.53	0.91	0.92	0.91	0.99

Abbreviation: AUC, area under curve.

Given the potential psychological impact of hair‐related issues on an individual's self‐esteem, it becomes crucial for biomedical engineers addressing such concerns to ensure a high level of certainty in their outcomes. Our proposed model significantly outperformed all prior models during the testing phase, showcasing an accuracy of 0.92, precision of 0.91, recall of 0.92, AUC of 0.99 and an F1‐score of 0.91.

Detailed model comparisons used in the tests are visually represented in Figure 3. The following section provides a comprehensive overview of the comparative performance of various CNN architectures for both validation and test sets, highlighting key metrics such as accuracy, loss, precision, recall, F1‐score, and AUC. These results collectively emphasize the superior performance of our proposed model over established methods, positioning our research as a significant advancement in the field of biomedical engineering.

FIGURE 3.

With this bar chart, it is easy to see how the results of all the models compare to each other on the test sets.

In our comparative analysis of the proposed model and alternative approaches, we utilized confusion matrices to meticulously evaluate their performance, as visually presented in Figure 4. Our investigation unequivocally highlighted the superior performance of the recommended model, substantiated by both numerical proportions and color depth within the confusion matrix. The model's robustness was evident through higher proportions along the diagonal, accentuated color depth, and a substantial number of images favoring the proposed approach.

FIGURE 4.

Confusion matrix of different models.

To enhance the interpretability of our findings, we undertook the normalization of the confusion matrix. This involved calculating proportions for each class by dividing the values in the respective row by the sum of that row. For instance, considering the first class of the proposed model, which comprises a total of six classes with seven samples in the first class:

$$\mathrm{Normalized}\mathrm{values}\mathrm{for}\mathrm{the}\mathrm{first}\mathrm{class}=\left[\frac{6}{7},\frac{0}{7},\frac{0}{7},\frac{0}{7},\frac{1}{7},\frac{0}{7}\right]$$

Systematically applying this normalization process to all classes provided a nuanced understanding of the model's performance, particularly in the intricate domain of medical imaging. Such comprehensive analyses, taking into account the total number of classes and samples per class, contribute to a robust evaluation of the proposed model, furthering our understanding of its effectiveness and reliability.

Furthermore, the precision‐recall curve in Figure 5 accentuates the effectiveness of our recommended model. The point of intersection between precision and recall values is notably positioned at the top right, distinctly surpassing the corresponding points for other models. This further validates the model's exceptional performance in accurately classifying instances, indicating its proficiency in both precision and recall metrics.

FIGURE 5.

In this graph, we can see how the precision and recall outcomes compare for each model that was used.

For a comprehensive examination of the performance metrics of our proposed model, including accuracy, loss, precision, recall, and AUC, we present graphical representations in Figures 6, 7, 8. These graphs provide an insightful visualization of the model's behavior across various metrics during the training and validation phases, offering a subtle understanding of its performance dynamics.

FIGURE 6.

On the left, we can see the accuracy during training and validation. On the right, we can see the loss during training and validation.

FIGURE 7.

On the left and right sides of the picture are graphs of training and validation precision and recall.

FIGURE 8.

AUC curve for proposed model which stands for “area under the curve”.

Our study presents compelling evidence of the robustness and effectiveness of our proposed model, showcasing superior performance across various metrics compared to alternative models. The graphs presented illustrate the model's progression, with a notable improvement in the training‐to‐validation curve ratio for accuracy over time, indicative of a refined learning process. The close alignment of loss values between training and validation data underscores the model's generalization capability. Despite initial challenges with overfitting in precision and recall, our model ultimately converges, highlighting its resilience and adaptability. The evaluation, as depicted in Figure 8, includes the AUC metric, demonstrating a commendable score close to 1. This signifies the model's stability across diverse data types, reinforcing its efficacy. Our proposed method, tailored for distinguishing hair and scalp diseases, emerges as a promising solution with seamless integration potential into real‐time systems.

In the examination of our proposed hair scalp disease classification model, the Figure 9 for the test set reveals its commendable performance across diverse disease categories. Notably, the model achieves remarkable precision scores, with perfect precision (1.00) for Acne Keloidalis Nuchae, Alopecia, Dissecting Cellulitis, and Effluvium. Additionally, the model demonstrates high recall rates, notably excelling in categories such as Alopecia, Effluvium and Dissecting Cellulitis. The F1‐scores further affirm the model's effectiveness, underscoring its ability to harmonize precision and recall metrics. While some categories exhibit optimal performance, such as Dissecting Cellulitis and Effluvium, others present opportunities for enhancement, as seen in the case of Folliculitis and Psoriasis. This comprehensive evaluation provides valuable insights into the model's strengths and areas for refinement, contributing to the ongoing advancements in the field of hair scalp disease classification.

FIGURE 9.

Model performance on hair scalp disease classification, showing precision, recall, and F1‐score metrics for six diseases, highlighting strengths, and areas for improvement.

In order to prove our model transparency, we used both Grad‐CAM and Saliency Map. A grid is used to highlight the main regions around the data picture. Figure 10 displays the images with their accompanying captions. For each image, we displayed both the original and the Grad‐CAM and Saliency Map outcomes. In all cases, we supplied both the true label and our estimate for the label of the graph. Overall, our research demonstrated that the proposed model was effective in determining which disease each image belonged to. The descriptions illuminated which features of the image most influenced the model's forecast and why.

FIGURE 10.

Explainable AI for all classes.

Model Performance on Hair Scalp Disease Classification, showing precision, recall, and F1‐score metrics for six diseases, highlighting strengths and areas for improvement.

5. DISCUSSION

This study introduces a transformative deep neural network model for diagnosing hair and scalp disorders, demonstrating superior performance compared to existing models such as VGG19, Inception, and DenseNet. The model's excellence is primarily underscored by its remarkable accuracy rate of 92%, meticulously evaluated on a diverse dataset encompassing various hair and scalp conditions. This accuracy significantly surpasses typical deep learning models, reflecting the robustness and effectiveness of our enhanced Xception‐based framework. Noteworthy enhancements, including ReLU activation, dense layers, and GAP, contribute to this substantial improvement in accuracy. In addition to accuracy, our model excels across various performance metrics, providing a comprehensive assessment of its diagnostic capabilities. Precision, recall, and F1‐score values for individual disease classes consistently exceed 80%, indicating the model's proficiency in accurately classifying instances. Specifically, precision rates range from 75% to 100%, recall rates from 86% to 100%, and F1‐scores from 75% to 100%. These metrics, essential for a nuanced evaluation, were meticulously derived from our comprehensive assessment on a curated dataset representative of diverse hair and scalp conditions. Furthermore, the model's efficiency extends to other critical metrics, including area under the receiver operating characteristic curve (AUC). Our model achieves an impressive AUC score of 0.99, indicative of its robust discriminatory power across various disease classes. This comprehensive evaluation ensures a holistic understanding of the model's performance, further supporting its potential application in clinical settings. A key differentiator in our approach is the integration of XAI techniques, such as Grad‐CAM and Saliency Map. These methods provide an unprecedented level of transparency to the model's decision‐making process, offering clear insights into the diagnostic factors considered by the AI. This interpretability is crucial for fostering clinical trust and adoption, addressing concerns related to the black‐box nature of many deep learning models. Moreover, the model's high precision, recall, and AUC scores have profound implications for dermatological practice, offering a more reliable and quicker diagnostic tool, particularly crucial for conditions where early detection significantly impacts treatment outcomes. The potential of our model to reduce misdiagnoses could lead to more effective patient management and possibly lower healthcare costs. As we look forward, continued model development remains essential. Future research should focus on expanding the dataset to include a wider array of conditions, enhancing the model's robustness and generalizability. The integration of our model into real‐world clinical settings for further validation and refinement, coupled with feedback from dermatology professionals, will be critical in its evolution and practical application. Overall, this model represents a significant advancement in applying AI to dermatology, outperforming existing models in accuracy, interpretability, and clinical applicability. It opens new avenues for improved patient care and sets the stage for future innovations in medical diagnostics.

6. CONCLUSIONS

The increasing prevalence and diagnostic challenges of hair and scalp disorders underscore the critical need for advanced, accurate diagnostic tools in dermatology. In response to the pressing need for improved diagnostic tools in dermatology, our research addresses the significant challenge of accurately diagnosing hair and scalp disorders. Traditional methods, constrained by subjectivity and limited accessibility, often lead to misdiagnoses, underlining the urgency for more advanced solutions. Our research presents a groundbreaking deep learning model, an enhanced version of the Xception architecture, which significantly outperforms existing diagnostic approaches and AI models. This model's superiority stems from specialized enhancements, including ReLU activation, dense layers, and GAP, specifically designed to navigate the intricate nature of dermatological imaging. Incorporating XAI techniques like Grad‐CAM and Saliency Map, our approach provides clarity and insight into the AI decision‐making process, fostering trust and interpretability, crucial for clinical adoption. The ability to accurately diagnose various hair and scalp conditions holds immense potential for improving patient care. By facilitating early and precise detection, our model significantly reduces the risks associated with misdiagnosis and the subsequent strain on healthcare resources. Future research should focus on broadening the dataset to encompass a more diverse range of conditions and ethnicities, enhancing the model's generalizability and accuracy. This expansion is crucial for realizing the full potential of AI in dermatology, particularly in underserved regions. In addition, the ultimate outcome can be providing ubiquitous access to simplified app functionality via mobile devices.

CONFLICT OF INTEREST STATEMENT

The authors declare no potential conflict of interests.

Chowdhury MS, Sultan T, Jahan N, et al. Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders. Skin Res Technol. 2024;30:e13660. 10.1111/srt.13660

DATA AVAILABILITY STATEMENT

Due to security concerns about research data, the datasets created, and/or analyzed during this work are not publicly accessible. However, they may be obtained from the corresponding author upon a reasonable request.