Table 2.
Evaluation of DL applications in medical imaging practices
| Authors | Dataset | Area | Techniques | Research confronts |
|---|---|---|---|---|
| Shin et al. [74] | Dynamic contras-enhanced MRI images | Identify numerous organ illness | Individual-layer SSAE | Inadequate data samples and structure are unsuccessful to be trained more multifaceted features |
| Bai et al. [75] | Dataset as short-axis cardiac MRI | Cardiac-image registration | Multi-atlas classification | It needs efficient computational potential |
| Cruz-Roa et al. [76] | Total 1417 Medical skin images | Diagnose cancerous cells in images | Classifier as softmax | Expelled implementing large superior Skin data sample |
| Shen et al. [77] | Lung image dataset syndicate | Lung-nodule classification | Multi-stage CNN approach | Preliminary trained, as well as the tested dataset, is remarkably dissimilar |
| Xu et al. [69] | The Case-Western Reserve University, US | Diagnosis of nuclei in the breast medical images | Stacked sparse auto- encoders (SSAE) | Need to enhance the withdrawal of some features from images |
| Guo et al. [66] | The University of Chicago Medical Center | Identification of Prostate | Stacked Sparse Auto- Encoders (SSAE) | Deliberate only 66 prostate images |
| Shin et al. [71] | The lung disease dataset with 905 images of 120 patients | Diagnose interspatial Lung ailments | Deep network CNN | Insufficient to contract with hypothetical work on cross-over modality data |
| Ghesu et al. [50] | 2891 Ultrasound aortic valve images of 869 patients | Segmentation and detection of the aortic valve | Marginal-space deep neural network | Unsuccessful to direct the computational restraints |
| Baumgartner et al. [67] | Total 1003 central-pregnancy scans | Detect fetal abnormality | Automated CNN | Evaluation metrics not estimated with existing methods |
| Payer et al. [78] | 895 X-ray images | Efficient response with landmark identification of the Images | Spatial-configuration net architecture | Methods to diminish the intricacy of the model are not discussed |
| Pratt et al. [79] | Kaggle database | Detection of diabetic retinopathy | CNN | The model failed to examine complex features |
| Abramoff et al. [80] | MESSIDOR standard DR DATASET | Diabetic retinopathy detection | IDX-Diabetic retinopathy V-X2.1 with CNN | Insufficient to maintain trained features by CNN model |
| Kawahara and Hamarneh. [81] | Dermofit image library contains 1300 skin images with 10 classes | Skin lesions detection | Multi-scale layered CNN | Debarred uses a large skin data sample |
| Rajpurkar et al. [62] | 30,805 patients with 1,12,120 X-ray Images | Radiology-based pneumonia identification | CheX-Net model as CNN | Other performance metrics should be considered |
| Oktay et al. [58] | UK-digital heart project, ACDC-17, CETUS-14 datasets | Image segmentation of Cardiac images | Deep Learning CNN | Little slice part resolution |
| Chee and Wu et al. [57] | Private MRI image datasets | Affine 3D image registration | Self-Supervised method on 3D medical images | Results lead to limited brain scans using axial visualization |
| Liao et al. [72] | Kaggle database | Diagnosis of lung cancer | 3D Neural Network | For minute nodules, it is hard to find highly accurate results |
| Guo et al. [60] | The Soft-Tissue Sarcomas Database | Detection of Tumor cells | Deep Network CNN | Analyzed only a single dataset on individual network |
| Seebock et al. [61] | Total 226 images with 33 healthy measurement | Retinal OCT segmentation | CNN | Need to be enhancing the version of learning models |
| Elmahdy et al. [56] |
Erasmus medical (EMC) and Haukeland medical consortium (HMC) datasets |
Prostate cancer image registration | Elastix- automated detection of prostate cancer using 3D deformable software with CNN | Methods that define to diminish the intricacy of the framework are not included |
| Zhu et al.[82] | Prostate MRI images—81 | Prostate-segmentation | Boundary-weight domain adaptive NN approach | Restricted dataset, model fall to attain examination of complex features |
| Shankar et al. [64] | MESSIDOR dataset | Diagnosing diabetic retinopathy in retinal fundus images | Synergic-deep learning model-based classification | Filtering techniques must be introduced during the pre-processing task and “AlexNet” and Inception methods in the enhanced version can improve the Hyperparameters |
| Qiao et al. [83] | Retinal fundus images | Diagnosis and prognosis of non-proliferative DR | Deep CNN, gaussian filtering, segmentation | The proposed method can be used to extract the features of texture, scale, form, etc |
| Komatsu et al. [84] | Fetal ultrasound images/videos | Diagnosis of cardiac deformities in ultrasound videos | Deep learning |
More datasets are needed for testing and validation purposes Proposed work must be obtained on other devices to check robustness It doesn’t hold fetal appearance in any position |
| Pan et al. [85] |
Total chest CT scan images-465 Moderate CT scan images-319 Severe CT scan images-146 |
Diagnosis of COVID-19 in chest CT scans | Deep learning, CNN, Novel COVID-lesions-net |
Training and Validations approaches must be obtained from Multi-Centered There must be Gold-Standard access to locate lesion areas |