|
| | Ref. | Methods | Metrics (%) | Performance analysis |
|
| CNN-based approaches | [20] | Hyperparameter tuning inception V4 model | Accuracy = 99
Specificity = 98
Sensitivity = 99
Precision = 97 | Need employment of versatile classification methods |
| [21] | Limited adaptive histogram equalization methods | Accuracy = 97
Specificity = 98
Sensitivity = 94
Precision = 97
F1 score = 94
G-Means = 94 | Image quality needs to be improvised |
| [23] | CNN-based binocular network | Specificity = 82
Sensitivity = 7
Kappa score = 0.824 | Availability of limited data leading to biased results |
| [25] | Capsule network | Accuracy = 95 | Training of limited image features |
| [26] | Fuzzy C-means | Accuracy = 98 | Inclusion of limited datasets |
| [33] | Feature extraction + segmentation methods | Accuracy, precision, and recall evaluated DR severity | Need for robust DL techniques to improve results |
| [34] | Deep CNN model | Sensitivity = 91
Specificity = 90 | System excluded possibilities implementation in clinical environment |
| [35] | Deep CNN with inception-V4 model | Accuracy = 88
Precision = 96
Recall = 94 | Information from other domains with plausible impact on results are not included |
| [37] | EffectiveNet method | AUC = 0.68
Kappa score = 0.36 | Biased results are generated from unbalanced dataset |
| [39] | Deep CNN with inception method | Accuracy = 92
Specificity = 94
Sensitivity = 81
Precision = 93 | Automated image prognosis method is not implemented |
|
| Transfer learning approaches | [24] | Transfer learning approach | Accuracy = 97
Specificity = 95
Sensitivity = 92 | Biased classification results |
| [38] | CNN with transfer learning | Accuracy = 74 | Inability to detect minor diseases |
|
| Deep neural network-based approaches | [22] | PCA with firefly algorithm using DNN | Accuracy = 97
Specificity = 95
Sensitivity = 92
Precision = 96
Recall = 96 | Low-dimensional data are not considered |
| [27] | DNN patch-based approach | Accuracy = 97
Specificity = 95
Sensitivity = 92 | Use of limited data leading to enhanced cost |
| [30] | Deep feature extraction method | Sensitivity = 94
Specificity = 98
AUC curve = 0.97 | High computational cost |
| [31] | DNN algorithms | Average precision (AP) = 0.88
IoU (intersection over union) = 0.17 | Lack of larger datasets |
| [32] | Score propagation method | Sensitivity = 91
Specificity = 90 | Image pixel score identification model is not included |
|
| Machine learning-based approaches | [29] | SVM and random forest techniques | Accuracy = 90 | Enhanced image classifier methods are not implemented |
| [36] | PCA with linear regression | Accuracy = 92 | Larger datasets are not considered |
|
