Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning
Abstract
:1. Introduction
- (1)
- The pre-trained Xception model is combined with the Deeplabv3 model. The output of these models is trained on the selected hyperparameters that are finalized after experiments for DR lesion segmentation.
- (2)
- Two transform learning models, efficient-net-b0 and squeeze-net, are employed for feature extraction from the selected fully connected layers such as MATMUL and pool-10, respectively.
- (3)
- The extracted features from MATMUL and pool-10 layers are fused in serial. The adequate features are determined using MPA.
- (4)
- For selection of the best features, the MPA model is trained on the selected hyperparameters. The selected features using the MPA model are passed to the KNN and NN classifiers for DR grade classification.
2. Related Work
3. Proposed Methodology
3.1. Proposed Semantic Segmentation Model
3.2. Classification of DR Lesions Using Deep Features
3.3. Feature Selection Using MPA
4. Experimental Discussion
- (1)
- Grade0 = 1092 images
- (2)
- Grade1 = 1224 images
- (3)
- Grade2 = 1976 images
- (4)
- Grade3 = 1016 images
4.1. Experiment 1: DR-Lesions Segmentation
4.2. Experiment 2: DR Lesions Classification
4.3. Significance Test
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Optimizer | Epochs for Training | Size Batch | Learning Rate | Error Rate |
---|---|---|---|---|
Sgdm | 200 | 32 | 0.0001 | 0.02 |
Adam | 16 | 0.04 | ||
RmsProp | 8 | 0.06 | ||
Momentum | 64 | 0.07 |
Lower Bound (lb) | 0 |
Upper Bound (ub) | 1 |
Threshold (thres) | 0.5 |
Levy Component (beta) | 1.5 |
Constant (P) | 0.5 |
Fish Aggregating Devices Effect (FADs) | 0.2 |
DR Levels | Description | Grades |
---|---|---|
Normal | Healthy | 0 |
Mild (NPDR) | Mas | 1 |
Moderate (NPDR) | Few MAs or HMs | 2 |
PDR | More MAs and HMs | 3 |
Datasets | Description |
---|---|
DIARETDB1 | Total images:89 Normal:5 Mild NPDR:84 |
e-ophtha-EX | Normal: 35 Exudates:47 |
IDRiD | MAs:81 HE:81 HMs:80 SoEX:40 |
Datasets | Lesions | mIoU | mDice | F1-Score | Precision (P) | Recall | Accuracy (Acc) |
---|---|---|---|---|---|---|---|
e-ophtha-EX | EX | 0.94 | 0.97 | 0.98 | 0.94 | 0.99 | 0.96 |
DIARETDB1 | HM | 0.87 | 0.83 | 0.72 | 0.87 | 0.99 | 0.87 |
HE | 0.71 | 0.83 | 0.92 | 0.71 | 0.99 | 0.71 | |
MA | 0.87 | 0.83 | 0.72 | 0.87 | 0.99 | 0.87 | |
SE | 0.86 | 0.88 | 0.87 | 0.86 | 1.00 | 0.86 | |
IDRiD | HM | 0.86 | 0.88 | 0.88 | 0.86 | 1.00 | 0.86 |
HE | 0.88 | 0.84 | 0.81 | 0.88 | 1.00 | 0.88 | |
MA | 0.71 | 0.83 | 0.92 | 0.71 | 1.00 | 0.71 | |
OD | 0.86 | 0.87 | 0.87 | 0.86 | 1.00 | 0.86 | |
SE | 0.84 | 0.83 | 0.82 | 0.87 | 0.98 | 0.97 |
Ref | Year | Method | Dataset | Results |
---|---|---|---|---|
[53] | 2022 | Dual-input attentive RefineNet (DARNet) | IDRiD | 0.95 Acc |
[67] | 2021 | U-Net | 0.87 Sensitivity | |
[68] | 2021 | U-Net | 0.83 Acc | |
[69] | 2021 | SVM | 0.80 Acc | |
Proposed Model | 0.96 Acc, 0.98 Sensitivity | |||
[53] | 2022 | DARNet | E-ophtha-EX | 0.96 Acc |
Proposed Model | 0.97 Acc | |||
[54] | 2022 | Nested U-Net | DIARETDB1 | 0.88 Sensitivity |
[52] | 2021 | MResUNet | 0.61 Sensitivity | |
[70] | 2020 | U-Net | 0.85 Sensitivity | |
Proposed Model | 0.99 Sensitivity |
Grade | Accuracy | Precision | Recall | F1 Score | Overall Accuracy | |
---|---|---|---|---|---|---|
Weighted KNN | 0 | 86.57% | 1.00 | 0.68 | 0.81 | 84.87% |
1 | 97.08% | 0.94 | 0.94 | 0.94 | ||
2 | 87.77% | 0.47 | 1.00 | 0.64 | ||
3 | 98.32% | 0.94 | 0.99 | 0.96 | ||
Optimizable KNN | 0 | 98.72% | 0.99 | 0.96 | 0.98 | 97.97% |
1 | 99.75% | 1.00 | 0.99 | 0.99 | ||
2 | 98.09% | 0.93 | 0.99 | 0.96 | ||
3 | 99.40% | 0.99 | 0.98 | 0.99 | ||
Cosine KNN | 0 | 96.75% | 0.99 | 0.91 | 0.95 | 93.66% |
1 | 95.56% | 0.84 | 0.99 | 0.90 | ||
2 | 96.53% | 0.94 | 0.91 | 0.93 | ||
3 | 98.48% | 0.98 | 0.96 | 0.97 | ||
Fine KNN | 0 | 97.94% | 1.00 | 0.94 | 0.96 | 97.13% |
1 | 99.29% | 0.99 | 0.98 | 0.99 | ||
2 | 97.73% | 0.91 | 0.99 | 0.95 | ||
3 | 99.29% | 0.98 | 0.99 | 0.98 |
Narrow Neural Network | Grade | Accuracy | Precision | Recall | F1 Score | Overall Accuracy |
0 | 95.80% | 0.96 | 0.90 | 0.93 | 89.31% | |
1 | 95.67% | 0.96 | 0.88 | 0.92 | ||
2 | 90.94% | 0.67 | 0.91 | 0.77 | ||
3 | 96.20% | 0.97 | 0.88 | 0.92 | ||
Medium Neural Network | 0 | 95.78% | 0.96 | 0.90 | 0.93 | 90.65% |
1 | 96.38% | 0.96 | 0.91 | 0.93 | ||
2 | 92.11% | 0.72 | 0.92 | 0.81 | ||
3 | 97.04% | 0.97 | 0.91 | 0.94 | ||
Wide Neural Network | 0 | 96.04% | 0.96 | 0.91 | 0.93 | 91.60% |
1 | 96.42% | 0.96 | 0.90 | 0.93 | ||
2 | 93.15% | 0.75 | 0.93 | 0.83 | ||
3 | 97.58% | 0.97 | 0.93 | 0.95 | ||
Bilayered Neural Network | 0 | 95.35% | 0.95 | 0.89 | 0.92 | 88.87% |
1 | 95.56% | 0.95 | 0.88 | 0.92 | ||
2 | 90.47% | 0.67 | 0.89 | 0.76 | ||
3 | 96.35% | 0.96 | 0.89 | 0.93 | ||
Trilayered Neural Network | 0 | 94.12% | 0.94 | 0.86 | 0.90 | 87.33% |
1 | 95.34% | 0.95 | 0.88 | 0.91 | ||
2 | 89.04% | 0.61 | 0.87 | 0.72 | ||
3 | 96.16% | 0.96 | 0.89 | 0.92 |
Ref# | Year | Method | Dataset | Overall Acc% | Grade-0% | Grade-1% | Grade-2% | Grade-3% |
---|---|---|---|---|---|---|---|---|
[71] | 2022 | WFDLN | Messidor | 0.98 | - | - | - | - |
[17] | 2019 | Modified Alexnet | - | 0.96 | 0.96 | 0.95 | 0.96 | |
[1] | 2021 | ResNet50 | - | 0.93 | 0.93 | 0.81 | 0.92 | |
[72] | 2021 | CapsNet | - | 0.97 | 0.97 | 0.97 | 0.98 | |
[73] | 2021 | Inception-ResNet-v2 | 0.72 | - | - | - | - | |
Proposed method | 0.99 | 0.98 | 0.99 | 0.98 | 0.99 |
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Shaukat, N.; Amin, J.; Sharif, M.; Azam, F.; Kadry, S.; Krishnamoorthy, S. Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning. J. Pers. Med. 2022, 12, 1454. https://doi.org/10.3390/jpm12091454
Shaukat N, Amin J, Sharif M, Azam F, Kadry S, Krishnamoorthy S. Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning. Journal of Personalized Medicine. 2022; 12(9):1454. https://doi.org/10.3390/jpm12091454
Chicago/Turabian StyleShaukat, Natasha, Javeria Amin, Muhammad Sharif, Faisal Azam, Seifedine Kadry, and Sujatha Krishnamoorthy. 2022. "Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning" Journal of Personalized Medicine 12, no. 9: 1454. https://doi.org/10.3390/jpm12091454
APA StyleShaukat, N., Amin, J., Sharif, M., Azam, F., Kadry, S., & Krishnamoorthy, S. (2022). Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning. Journal of Personalized Medicine, 12(9), 1454. https://doi.org/10.3390/jpm12091454