Abstract
The most common human parasite as per the medical experts is the malarial disease, which is caused by a protozoan parasite, and Plasmodium falciparum, a common parasite in humans. A microscopist with expertise in malaria diagnosis must conduct this complex procedure to identify the stages of infection. This epidemic is an ongoing disease in some parts of the world, which is commonly found. A Kaggle repository was used to upload the data collected from the NIH portal. The dataset contains 27558 samples, of which 13779 samples carry parasites and 13779 samples do not. This paper focuses on two of the most common deep transfer learning methods. Unlike other feature extractors, VGG-19's fine-tuning and pretraining made it an ideal feature extractor. Several image classification models, including VGG-19, have been pretrained on larger datasets. Additionally, deep learning strategies based on pretrained models are proposed for detecting malarial parasite cases in the early stages, in addition to an accuracy rating of 98.34∗ 0.51%.
1. Introduction
The leading cause of infection in all parts of the world is malaria, a deadly disease. The rapid consumption and high mortality rate of this epidemic condition have been documented throughout history. The global death toll from malaria was estimated at 4,29,000. In 2015, there were an estimated 3,03,000 children under the age of five years, and prenatal women are the most at risk of death [1]. By detecting this disease at an early stage, the death rate can be reduced and prevented. Researchers face a challenge in providing the most accurate parasite detection in the shortest amount of time, cost, and effort. During the last few decades, this visual inspection has played a vital role in being a tool in the health check field for decision making. A thick blood smear can identify malaria parasites in blood samples [2]. This shows that it is close to eleven times more sensitive than a thin blood smear for the rapid detection of parasites. It is often used to test the development stages to create blood smears placed on a microscope glass slide. To confirm malaria infection, a pathologist uses a light microscope to identify changes in the size, shape, and perception of various RBCs. The pathologist's understanding of the disease depends on the accuracy of a plasmodium microscopic report. This technique is arduous and inefficient due to uncertainty, which can cause erroneous and contradictory diagnoses, as well as inappropriate medication and, in rare situations, the death of the patient and specimens of infected and noninfected microbes.
A mechanism for detecting malaria infection has been proposed using supervised learning. The methods for detecting malaria parasites were demonstrated by using in vitro culture image samples [3]. The samples developed in a laboratory do not contain substances other than WBCs, platelets, or parasites. When considering the severity of malaria depending on the number of deaths caused by the disease, it is reasonable to accept possible minor errors caused by an automated method during execution [4, 5]. Since the advent of deep learning techniques, feature extraction has been made far more efficient than traditional methods. Because deep learning methods still require trained experts and advanced techniques for calculating disease prediction, most of these methods still require efficient feature extraction optimization. There are many layers and levels of nonlinear mapping in CAD schemes based on ANN architecture. As a result of the layer-wise network of hidden layers, gradient-based optimization produces poor results [6, 7]. Microscopic diagnosis requires extensive training, experience, and skills. In rural areas where malaria is prevalent, manual microscopy has not proven to be an effective screening tool when performed by nonexperts [8–10]. This model was intended to enhance the model's performance by modifying the network architecture and hyper-tuning the features to achieve a better-performing model. To determine the key features in the future, this paper focuses on the network architecture. The basic VGG-19 model obtains 85% accuracy, but after fine-tuning the model and applying the data augmentation technique to the training dataset, it can attain 97.14%.
The following is the sequence under which this manuscript is structured. Section 2 represents the related work and data acquisition. Section 3 illustrates the proposed deep transfer learning methodology. Section 4 discusses the findings. Section 5 concludes the paper.
2. Related Work
There are several methods of identifying parasite RBCs. Purwar et al. preprocessed by utilizing local histograms [11]. The Hough transform and morphological operations are used for segmentation and classification of diseased and clean cells. Di Ruberto et al. employed a statistical k-means clustering algorithm [12]. Using thresholding, Ritter and Cooper [13] have segmented cells, separated overlaps, and modified division lines according to Dijkstra's algorithm. Diaz et al. [14] applied efficiency to develop templates from parasite-stained images, which would then be used to classify each cell's infection life stages. RBCs can be segmented from the background in blood images using a variety of techniques. Díaz et al. [15] determined whether the RBCs were infected by the parasite from the host or not by separating them. Savkare et al. [16] and Ross et al. [17] analyzed grayscale images using k-means and k-medians to define the overall clusters into two parts. The significance of supervised learning is to identify classes of sample data. These disease stages were identified with a blend of ML algorithms. Krizhevsky et al. [18] built a well-known CNN architecture, that is, the ALEX net, to compete with ImageNet and bagged awards. Quinn et al. [19] compared the CNN with a classifier using trees, and the accuracy rate was determined by analyzing the middle of the operating region. To identify blood cells on blood smear images, Chowdhury et al. [20] adopted a CNN approach to detect infections that were affected by the blood smears. Raviraja et al. [6] analyzed pretrained CNN models for malaria detection in blood cell images, namely, DenseNet121, VGG-16, Alexnet, ResNet50, FastAI, and ResNet101, Meng et al. [21]. With a high precision of 97.5%, ResNet50 excelled over the other CNN models [22]. To conclude, many deep learning algorithms for detecting malaria using cell images have been presented by Yue [23]. Many of them used large pre-trained CNN models to enhance the accuracy of classification, whereas others used customized CNNs to minimize the computational time [24].
The implications of substantial quantities of improperly classified data in medical image classification are catastrophic, and the objective of proposing a medical diagnosis tool is wrecked. In addition to efficiency, additional parameters such as F1 score [25, 26], area under the curve (AUC) [27, 28], sensitivity [29, 30], and specificity [31–33] are vital in analyzing various approaches. A random sample of cell images infected/not infected with malaria is shown in Figure 1.
Figure 1.
A random sample of cell images infected/not infected with malaria [2].
2.1. Data Acquisition
The data was obtained from the National Institutes of Health portal and uploaded to a Kaggle repository. There are 27558 cell images in the dataset, out of which 13779 are malaria-infected cell images and another 13779 are malaria-free cell images. The random samples were collected and separated into three sets: training, testing, and validation. Next, 8000 images were deployed for training and 3000 images for validation for each class. These 11000 images have been used to train models. Subsequently, these were used along with the remaining 2779 images for each class as test data to evaluate the proposed models to perform on images that have never been seen before, because the original dataset included images with different dimensions, and it was scaled to equal dimensions before splitting the data. Most of the images were scaled to 128 × 128 pixels with three channels of RGB. Having all images equal will allow the neural networks to learn more quickly and with minimal mistakes in the future. The outcomes obtained excelled all existing methods, so data augmentation was used to improve the results. A few of the images in the class folders at the beginning are significantly different than the ones at the end. As a result, this was used to sample the data at random, as this will allow the proposed models to learn more diversified features from both classes, which will reduce overfitting and make our model more extensible to data.
3. Proposed Model
Morphological image processing is used to eliminate noise and recreate object features (see Figure 2). To achieve deep feature extraction and transfer learning, the convolutional layers are frozen. To classify RBCs, a fine-tuned pretrained CNN model with image augmentation is proposed (see Figure 3).
Figure 2.
Morphological filters applied to malaria cells.
Figure 3.
Proposed CNN-model.
3.1. Transfer Learning
The paths, according to K. Fukushima, are the beginning of a deep convolutional neural network architecture. The idea has been around for a while, but due to the lack of efficient high computational power, it has yet to gain momentum. In recent times, graphics processing units have advanced significantly toward high-performance computing technologies. Computational intelligence techniques have gained prominence as a result of their high possibility. CNN is the most popular type of this approach, which is composed of the layers described below.
Three main CNN layers were used in this algorithm, namely, “convolution,” “pooling,” and “fully connected layers.” Figure 3 exhibits a schematic representation of a model with one conventional layer and one maximum layer. In the feature map, activation functions are used to increase the nonlinearity of the network ReLU. A neuron with the sigmoid activation function appears in the output layer of the model. When all negative values in the activation map are replaced with zero, ReLU activation completely cancels out all negative values. A binary classification model is built using sigmoid activation with a loss of function of binary cross-entropy. It has a learning rate of 0.01. In this case, the function gives a value between 0 and 1. Following the compilation of the dataset, the model will be trained based on the inputs received from the training samples, which will map the inputs to the outputs. It is a matter of choosing a set of weights that is best suited for resolving these problems.
3.2. Predictable Method
The initial model is trained by this network model using traditional methods of training for a given number of epochs. Model accuracies of 99.80% during training and 95.60% during validation were unchanged. Figures 4 and 5 show the exactness and failure graphs of the model. AUC score, specificity, sensitivity, and test accuracy were used to evaluate the performance. Table 1 depicts an implementation of the test set, whereas Table 2 exhibits CNN model architecture. The input layer a = i1 and output layer io1 = 3 and p1 = 0, input layer c2d, output layer co1 = 32 and p1 = 896, max input layer mp2d and output layer mo2 = 32, and p1 = 0. The input layer c2d1, output layer c2do1 = 64 and p2 = 18496, and the input layer mp2d1 and output layer mpo1 = 64 and p2 = 0. The input layer c2d2, output layer c2o2 = 128, and p3 = 73856. The input layer = m2d2, output layer = m2do2 = 128, and p4 = 0. The input layer flattened layer = Fl, output layer flo = 28800, and p4 = 0. Input the dense layer dl, output layer det5 = 512, and p5 = 14746112. The input layer dropout = dot, output layer dot5 = 512, and p5 = 0. The input layer dns_1 and output layer dnst5 = 512 and p6 = 262656.
Figure 4.
Model accuracy performance of CNN model.
Figure 5.
Model loss performance of CNN model.
Table 1.
Performance test.
| Metrics | Performance (%) |
|---|---|
| Testing-accuracy | 95.56 |
| F1 Score | 96.45 |
| A∪C score | 95.45 |
| Sensitivity | 96.65 |
| Specificity | 95.25 |
Table 2.
Convolution neural network model.
| Layer | Output | Parameter |
|---|---|---|
| i_1 | [(N, 125, 125, io1)] | P0 |
| c2d | (N, 125, 125, co1) | P1 |
| mp2d | (N, 62, 62, mpo2) | p01 |
| c2d_1 | (N, 62, 62, c2do1) | P2 |
| mp2d _1 | (N, 31, 31, mp2o1) | p02 |
| c2d2 | (N, 31, 31, c2o2) | P3 |
| m2d_2 | (N, 15, 15, m2do) | p03 |
| Fl | (N, flo) | P4 |
| Dl | (N, det5) | 14746112 |
| Dt | (N, dot5) | p05 |
| dns_1 | (N, dnst5) | 262656 |
| Dot | (N, dot5) | d05 |
| dense_2 | (N, 1) | 513 |
| Total | — | 15,102,529 |
| Trainable | — | 15,102,529 |
| Non-trainable | — | 0 |
Note: _1 = “input1”, c2d = “convolutional2d”, mp2d = “ max_poolint2d”, c2d_1 = “convolutional2d1”, mp2d _1 = “max_poolint2d1”, c2d2 = “convolutional2d2 “, m2d_2 = “max_poolint2d2 “, f1 = “Flatten Layers”, d1 = “ dropout”, dt = “ dense1”, dns_1 = “dense2”, dot = “dropout1”. io1 = “3”, co1 = “ “, mpo2 = “ 32”, c2do1 = “64 “, mp2o1 = “64 “, c2o2 = “128 “, m2do = “ 128”,flo = “ 28800”, det5 = “512 “, dot5 = “512 “, dnst5 = “ 512”, p0 = “0”, p1 = “ 896”, p01 = “0”, p2 = “ 18496”, p02 = “0”, p3 = “ 73856”, p03 = “0”, p4 = “ 0”, p05 = “0”, d05 = “0”.
3.3. Pretrained CNN Model
Figures 6 and 7, respectively, demonstrate the accuracy model and performance for the VGG-19 pretrained model and weights as displayed in Table 3. The convolutional layers are divided into sixteen layers, and there are 3 ∗ 3 convolutional filters. The input layer b1-c1, output layer t1 = 64 and parameter p1 = 1792, the input layer b1-c2, output layer t1 = 64 and parameter p1 = 3698, and b1_pool and output t1 = 64 and parameter p1 = 0. The input layer b2-c1, the output layer t2 = 128, and the parameter p2 = 73856. The input layer b2-c2, the output layer t1 = 128, and parameters p1 = 147584 and b2_pool and output t2 = 1284 and parameter p2 = 0. The input layer b3-c1, output layer t3 = 256, and parameters p3 = 295168, b3_c2 output layer t3 = 256, and p3 = 590080, b3-c3 and b3-c4 output layer t3 = 256, and output layer p4 = 590080. The b3-pool and output t3 = 256 and p3 = 0. The input layer b4-c1, the output layer t4 = 512, and p4 = 1180160. The input layer b4-c2, b4-c3, b4-c4 = 512 and p4 = 2359808. The input layer b4-pool and t4 = 512 and the output layer p4 = 0. The input layer b5-c1, b5-c2, b5-c3, b5-c4, and t5 = 512, and the output layer p5 = 2359808. The input layer fla-1 and the output layer t5 = 4608 and p5 = 0. The input layer dse3 and output layer dset = 512 and p5 = 2359808. The input layer dtt2 and the output layer dtt5 = 512 and p5 = 0. The input layer dse4 and output layer dset5 = 512 and p5 = 262656. In addition to that, there are max pooling filters for downscaling and two fully connected hidden layers with 4096 units each. The remaining dense layer consists of one thousand units, each representing one of the image categories in ImageNet. The dense layer is fully connected, so the last three layers are skipped, and the five layers are concentrated to use the vgg19 model for feature extraction. This model was built from scratch using the original datasets that include all 19 trainable layers. The ReLU served as the activation function for the network. The Adam optimization was used to create the loss function. The model was built using transfer learning and using pre-trained frozen layers. Later, this model was used to generate the output of the images. This was implemented using the sigmoid activation function as a simple feature extractor by freezing all five convolutional blocks to prevent the weights from moving across epochs. This third model is fine-tuned and is built by freezing the first three blocks from the image net and then training blocks four and five from the malarial datasets. To fine-tune the VGG-model, blocks 4 and 5 were changed so that their weights are updated each time the model is evaluated. These preprocessing strategies were applied to this model, which includes normalization, data augmentation, and standardization. A sigmoid activation function with two methods was applied to solve the classification problem to gain an output of 1 for infected and 0 for healthy.
Figure 6.
Accuracy performance of modified CNN model.
Figure 7.
Loss performance of modified CNN model.
Table 3.
Pretrained convolution neural network model (VGG-19).
| Layer | Output | Parameter |
|---|---|---|
| input_2 | [(N, 125, 125,3)] | 0 |
| b1-c1 | (N, 125, 125, t1) | P1 |
| b1-c2 | (N, 125, 125, t1) | P1 |
| b1-pool | (N, 62, 62, t1) | 0 |
| b2-c1 | (N, 62, 62, t2) | P2 |
| b2-c2 | (N, 62, 62, t2) | P2 |
| b2-pool | (N, 31, 31, t3) | 0 |
| b3-c1 | (N, 31, 31, t4) | P3 |
| b3-c2 | (N, 31, 31, t4) | P3 |
| b3-c3 | (N, 31, 31, t4) | P3 |
| b3_c4 | (N, 31, 31, t4) | P3 |
| b3-pool | (N, 15, 15, t4) | 0 |
| b4-c1 | (N, 15, 15, t5) | P4 |
| b4-c2 | (N, 15, 15, t5) | P4 |
| b4-c3 | (N, 15, 15, t5) | P4 |
| b4-c4 | (N, 15, 15, t5) | P4 |
| b4-pool | (N, 7, 7, t5) | 0 |
| b5-c1 | (N, 7, 7, t5) | P5 |
| b5-c2 | (N, 7, 7, t5) | P5 |
| b5-c3 | (N, 7, 7, t5) | P5 |
| b5-c4 | (N, 7, 7, t5) | P5 |
| b5-pool | (N, 3, 3, t5) | 0 |
| fla_1 | (N, t5) | 0 |
| Dse-3 | (N, dset5) | P5 |
| Dt-2 | (N, dttt5) | 0 |
| Dse-4 | (N, dset5) | 262656 |
| Dt-3 | (N, dt5) | 0 |
| Dse-5 | (N, 1) | 513 |
| Total params: | 22,647,361 | |
| Trainable params: | 2,622,977 | |
| Nontrainable: | 20,024,384 |
Note: b1-c1 and b1-c2 = “ block1_conv1 and block1_conv2”, b2-c1 and b2-c2 = “ block2_conv1 and block2_conv2”, b3-c1 and b3-c2 and b3-c3 and b3-c4 = ” block3_conv1 and block3_conv2 and block3_conv3 and block3_conv4”, b5-c1 and b5-c2 and b5-c3 and b5-c4 and b5-c5 = ” block5_conv1 and block5_conv2 and block5_conv3 and block5_conv4”. fla1 = ” flatten_1” t5 = 4608. dse3, dse4, dse5 = ” dense_3, dense_4, dense_5”, dt2 = ” droupout_2.
3.4. Image Augmentation with a Fine-Tuned Pretrained Model
In Figure 8, the existing images from the training samples were reworked and transformed to create a new, modified version of the originals because of rotation, shearing, translation, zooming, and so on. Figures 6 and 7 illustrate how the random transformation, model accuracy, and loss of the model are what are required to obtain the same images every time. The augmentation of the model accuracy is shown in Figures 9 and 10.
Figure 8.
Sample augmented images.
Figure 9.
Augmentation accuracy results.
Figure 10.
Augmentation loss results.
The confusion matrix (FN) is used to evaluate the number of positive and negative predictions as shown in Figure 11. The confusion matrix is used to determine whether a prediction is a truly positive or true negative.
Figure 11.
Confusion matrix results.
4. Discussion
Individual red blood cell smear images are investigated to evaluate if they are infected or healthy. The study comprises a range of pretrained convolutional neural networks with transfer learning that are fine-tuned and registered on the malaria dataset. On VGG-19 and Transfer Learning, research shows that different preprocessing approaches like normalization and scaling do not affect model performance, however, the data augmentation technique has shown encouraging outcomes. The basic VGG-19 model obtains 85% accuracy, but after fine-tuning the model and applying the data augmentation technique to the training dataset, it can attain 97.14%, as shown in Table 4. Transfer learning is a wonderful strategy that can be used to create promising results, according to the research and the performance analyses depicted in Table 5.
Table 4.
Confusion matrix-based analyses.
| Models | Accuracy | F1 score | Precision | Recall |
|---|---|---|---|---|
| Basic CNN | 0.9397 ± 0.23 | 0.9397 ± 0.13 | 0.9397 ± 0.19 | 0.9397 ± 0.27 |
| VGG-19 frozen | 0.9486 ± 0.13 | 0.9482 ± 0.12 | 0.9456 ± 0.15 | 0.9480 ± 0.12 |
| VGG-19 fine-tuned | 0.9704 ± 0.06 | 0.9640 ± 0.06 | 0.9740 ± 0.07 | 0.9700 ± 0.03 |
Table 5.
The performance report of the model classification.
| Precision | Recall | F1 score | Support | |
|---|---|---|---|---|
| Healthy sample | 0.97 | 0.96 | 0.96 | 4085 |
| Malaria-sample | 0.96 | 0.96 | 0.95 | 4173 |
| Micro-average | 0.97 | 0.97 | 0.97 | 8158 |
| Macro-average | 0.97 | 0.97 | 0.97 | 8158 |
| Weighted-average | 0.97 | 0.97 | 0.97 | 8158 |
5. Conclusion
The deep learning neural network model was applied to improve the model's performance. It was shown that standardization and normalization had less impact on classification. The use of data augmentation improved the model performance and yielded positive results. Models of VGG-19 and ImageNet were derived from the initial concept using the combination of transfer learning and parameter tuning. To determine the key features, this paper has focused on the network architecture. This was intended to enhance the model's performance by modifying the network architecture and hyper-tuning the features to achieve a better-performing model.
Acknowledgments
This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R120), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
References
- 1.Boschi-Pinto C., Dilip T. R., Costello A. Association between community management of pneumonia and diarrhoea in high-burden countries and the decline in under-five mortality rates: an ecological analysis. BMJ Open . Feb 2017;7(2) doi: 10.1136/bmjopen-2016-012639.e012639 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Poostchi M., Silamut K., Maude R. J., Jaeger S., Thoma G. Image analysis and machine learning for detecting malaria. Translational Research: The Journal of Laboratory and Clinical Medicine . Jan 2018;194:36–55. doi: 10.1016/j.trsl.2017.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Das D. K., Ghosh M., Pal M., Maiti A. K., Chakraborty C. Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron . 2013;45:97–106. doi: 10.1016/j.micron.2012.11.002. [DOI] [PubMed] [Google Scholar]
- 4.Makkapati V. V., Rao R. M. Segmentation of malaria parasites in peripheral blood smear images. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing; April 2009; Taipei, Taiwan. pp. 1361–1364. [DOI] [Google Scholar]
- 5.Mustare N., Kaveri Sreelathareddy V., Sreelathareddy V. Development of automatic identification and classification system for malaria parasite in thin blood smears based on morphological techniques. Proceedings of the 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017 Development of Automatic Identification and Classification System for Malaria Parasite in Thin Blood Smears Based on Morphological Techniques; September 2017; Chennai, India. pp. 3006–3011. [DOI] [Google Scholar]
- 6.Raviraja S., Osman S. S., Kardman A novel technique for malaria diagnosis using invariant moments and by image compression. In: Abu Osman N. A., Ibrahim F., Wan Abas W. A. B., Abdul Rahman H. S., Ting H. N., editors. Proceedings of the 4th Kuala Lumpur International Conference on Biomedical Engineering 2008; January 2008; Kuala Lumpur, Malaysia. Springer Berlin Heidelberg; pp. 730–733. [DOI] [Google Scholar]
- 7.Somasekar J., Rama Mohan Reddy A., Sreenivasulu Reddy L. An efficient algorithm for automatic malaria detection in microscopic blood images. In: Krishna P. V., Babu M. R., Ariwa E., editors. Global Trends in Information Systems and Software Applications . Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. pp. 431–440. [DOI] [Google Scholar]
- 8.Nair V., Hinton G. E. 3d object recognition with deep belief nets. Proceedings of the 22nd International Conference on Neural Information Processing Systems; 2009; Red Hook, NY, USA. NIPS’09, Curran Associates Inc; pp. 1339–1347. [Google Scholar]
- 9.Prathyakshini A., Akshaya C. V. Classification and clustering of infected leaf plant using K-means algorithm. In: Nagabhushan T., Aradhya V. N. M., Jagadeesh P., Shukla S., Chayadevi M. L., editors. Cognitive Computing and Information Processing . Singapore: Springer Singapore; 2018. pp. 468–474. [DOI] [Google Scholar]
- 10.Arel I., Rose D. C., Karnowski T. P. Deep machine learning - a new Frontier in artificial intelligence research [research Frontier] IEEE Computational Intelligence Magazine . 2010;5(4):13–18. doi: 10.1109/MCI.2010.938364. [DOI] [Google Scholar]
- 11.Purwar Y., Shah S. L., Clarke G., Almugairi A., Muehlenbachs A. Automated and unsupervised detection of malarial parasites in microscopic images. Malaria Journal . 2011;10(1):p. 364. doi: 10.1186/1475-2875-10-364. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Di Ruberto C., Dempster A., Khan S., Jarra B. Automatic thresholding of infected blood images using granulometry and regional extrema. Proceedings of the 15th International Conference on Pattern Recognition. ICPR-2000; September 2000; p. p. 441. [DOI] [Google Scholar]
- 13.Ritter N., Cooper J. Segmentation and border identification of cells in images of peripheral blood smear slides. Proceedings of the Thirtieth Australasian Conference on Computer Science. AuthorAnonymous, 2007; August 2007; Australia. Australian Computer Society, Inc; pp. 161–169. [Google Scholar]
- 14.Díaz G., González F. A., Romero E. A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images. Journal of Biomedical Informatics . 2009;42(2):296–307. doi: 10.1016/j.jbi.2008.11.005. [DOI] [PubMed] [Google Scholar]
- 15.Díaz G., Gonzalez F., Romero E. Infected cell identification in thin blood images based on color pixel classification: comparison and analysis. Proceedings of the Congress on Pattern Recognition, 12th Iberoamerican Conference on Progress in Pattern Recognition, Image Analysis and Applications; 2007; Berlin, Heidelberg. Springer-Verlag; pp. 812–821. [Google Scholar]
- 16.Savkare S. S., Narote A. S., Narote S. P. Automatic blood cell segmentation using k-mean clustering from microscopic thin blood images. Proceedings of the Third International Symposium on Computer Vision and the Internet; 2016; New York, NY, USA. VisionNet’16, Association for Computing Machinery; pp. 8–11. [DOI] [Google Scholar]
- 17.Ross N. E., Pritchard C. J., Rubin D. M., Dusé A. G. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical, & Biological Engineering & Computing . Apr 2006;44(5):427–436. doi: 10.1007/s11517-006-0044-2. [DOI] [PubMed] [Google Scholar]
- 18.Krizhevsky A., Sutskever I., Hinton G. E. Imagenet classification with deep convolutional neural networks. Communications of the ACM . May 2017;60(6):84–90. doi: 10.1145/3065386. [DOI] [Google Scholar]
- 19.Quinn J. A., Nakasi R., Mugagga P. K. B., Byanyima P., Lubega W., Andama A. Deep convolutional neural networks for microscopy-based point-of-care diagnostics. 2016. http://arxiv.org/abs/1608.02989 .
- 20.Chowdhury A. B., Roberson J., Hukkoo A., Bodapati S., Cappelleri D. J. Automated complete blood cell count and malaria pathogen detection using convolution neural network. IEEE Robotics and Automation Letters . 2020;5(2):1047–1054. doi: 10.1109/lra.2020.2967290. [DOI] [Google Scholar]
- 21.Meng L., Aravinda C. V., Uday Kumar Reddy K. R., Izumi T., Yamazaki K. Ancient Asian character recognition for literature preservation and understanding. In: Ioannides M., Fink E., Brumana R., et al., editors. Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection . Cham: Springer International Publishing; 2018. pp. 741–751. [DOI] [Google Scholar]
- 22.Shalini H., Aravinda C. V. An IoT-based predictive analytics for estimation of rainfall for irrigation. In: Chiplunkar N. N., Fukao T., editors. Advances in Artificial Intelligence and Data Engineering . Singapore: Springer Singapore; 2021. pp. 1399–1413. [DOI] [Google Scholar]
- 23.Yue X, Li H, Saho K, Uemura K, Aravinda C V, Meng L. Machine learning based apathy classification on Doppler radar image for the elderly person. Procedia Computer Science . 2021;187:146–151. doi: 10.1016/j.procs.2021.04.045. https://www.sciencedirect.com/science/article/pii/S1877050921008255 . [DOI] [Google Scholar]
- 24.C V A., Meng L., Masahiko A., Kumar U., Prabhu A. A complete methodology for k historical character recognition using multiple features approach and deep learning model. International Journal of Advanced Computer Science and Applications . 2020;11(8) doi: 10.14569/IJACSA.2020.0110884. [DOI] [Google Scholar]
- 25.Jie D., Zheng G., Zhang Y., Ding X., Wang L. Spectral kurtosis based on evolutionary digital filter in the application of rolling element bearing fault diagnosis. International Journal of Hydromechatronics . 2021;4(1):27–42. doi: 10.1504/ijhm.2021.114173. [DOI] [Google Scholar]
- 26.Xu, Li C., Li C. Electric window regulator based on intelligent control. Journal of Artificial Intelligence and Technology . 2021;1:198–206. doi: 10.37965/jait.2020.0045. [DOI] [Google Scholar]
- 27.Kaur M., Singh D., Kumar V., Gupta B. B., Abd El-Latif A. A. Secure and energy efficient-based E-health care framework for green internet of things. IEEE Transactions on Green Communications and Networking . 2021;5(3):1223–1231. doi: 10.1109/tgcn.2021.3081616. [DOI] [Google Scholar]
- 28.Mondal S. C., Marquez P. l. C., Tokhi M. O. Analysis of mechanical adhesion climbing robot design for wind tower inspection. Journal of Artificial Intelligence and Technology . 2021;1:219–227. doi: 10.37965/jait.2021.0013. [DOI] [Google Scholar]
- 29.Kaur M., Singh D. Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks. Journal of Ambient Intelligence and Humanized Computing . 2021;12(2):2483–2493. doi: 10.1007/s12652-020-02386-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Balakrishna A., Mishra P. K. Modelling and analysis of static and modal responses of leaf spring used in automobiles. International Journal of Hydromechatronics . 2021;4(4):350–367. doi: 10.1504/ijhm.2021.120616. [DOI] [Google Scholar]
- 31.Singh P. K. Data with non-Euclidean geometry and its characterization. J. Artif. Intell. Technol. . 2022;2:3–8. [Google Scholar]
- 32.Singh D., Kumar V., Kaur M., Jabarulla M. Y., Lee H.-N. Screening of COVID-19 suspected subjects using multi-crossover genetic algorithm based dense convolutional neural network. IEEE Access . 2021;9(2021):142566–142580. doi: 10.1109/access.2021.3120717. [DOI] [Google Scholar]
- 33.Hahn T. V., Mechefske C. K. Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder. International Journal of Hydromechatronics . 2021;4(1):69–98. doi: 10.1504/ijhm.2021.114174. [DOI] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon request.