## 1. Bare-bones project definition **Input:** A retinal fundus image. **Output:** 1. `Image quality` → Accept / Reject 2. `DR grade` → Level 0–4 3. `Referable DR` → Yes / No 4. `Confidence` 5. `Explanation` → Grad-CAM / lesion evidence 6. Eventually: an automated screening report ```text Fundus Image │ ▼ [Preprocessing] │ ▼ [Image Quality Check] │ ├── Poor → Reject / Recapture │ ▼ [DR Classification Model] │ ├── Grade 0 ├── Grade 1 ├── Grade 2 ├── Grade 3 └── Grade 4 │ ▼ [Referable?] │ ▼ [Grad-CAM + Confidence] │ ▼ [Screening Report] ``` The segmentation/lesion-detection components can then be added incrementally. --- # 2. Split the project into modules ```text ml/ │ ├── data/ │ ├── raw/ │ ├── processed/ │ ├── train/ │ ├── validation/ │ └── test/ │ ├── preprocessing/ │ ├── quality_assessment │ ├── illumination_normalization │ ├── denoising │ └── enhancement │ ├── models/ │ ├── quality_model/ │ ├── dr_classifier/ │ ├── lesion_detection/ │ └── segmentation/ │ ├── training/ │ ├── train_quality_model │ ├── train_classifier │ └── evaluate │ ├── explainability/ │ ├── gradcam │ ├── confidence │ └── lesion_evidence │ ├── pipeline/ │ └── inference │ ├── reports/ │ └── report_generation │ ├── simulink/ │ ├── evaluation/ │ ├── metrics │ ├── confusion_matrix │ └── benchmark_comparison │ └── README.md ``` --- # 3. First milestone ### Milestone 1 — Minimum viable ML system ```text Image ↓ Preprocessing ↓ DR classifier ↓ 0–4 prediction ↓ Referable / non-referable ↓ Confidence ``` Just: > **Given a fundus image, can our model reliably classify its DR severity?** --- # 4. Dataset We need images with **ground-truth DR severity labels** corresponding as closely as possible to: ```text 0 → No DR 1 → Mild NPDR 2 → Moderate NPDR 3 → Severe NPDR 4 → Proliferative DR ``` The problem statement explicitly expects this five-level classification. For the initial prototype, the most important dataset properties are: * fundus photographs * severity labels * sufficient samples per class * preferably publicly documented * preferably with established use in DR research * train/test separation that prevents leakage **Do not start training until the dataset and label mapping are written down.** --- # 5. Two separate ML problems ### Problem A — 5-class severity ```text Image → {0,1,2,3,4} ``` ### Problem B — screening decision ```text Image → {Non-referable, Referable} ``` where: ```text 0, 1 → Non-referable 2, 3, 4 → Referable ``` The problem statement specifically defines Level 2+ as referable. Our model can therefore produce: ```text Predicted class: 3 Confidence: 0.87 Referable DR: YES ``` --- # 6. Model plan Use transfer learning. Conceptually: ```text Pretrained CNN │ ▼ Fundus image │ ▼ Feature extraction │ ▼ Classification head │ ▼ 5 DR classes ``` Possible backbone families can be decided later based on MATLAB support and compute availability. Our initial experiment should answer: > Can a pretrained image-classification network fine-tuned on the chosen dataset achieve useful DR classification performance? Only after that should we start experimenting with architectures. --- # 7. Preprocessing pipeline ```text Raw image ↓ Resize ↓ Crop / remove irrelevant borders ↓ Normalize ↓ Optional contrast enhancement ↓ Model ``` Later we'll investigate: ```text CLAHE illumination normalization denoising color normalization etc. ``` The problem statement specifically calls for CLAHE, illumination normalization and denoising as possible enhancement techniques. --- # 8. Image-quality module This should be a separate model/module rather than mixing it into the DR classifier initially. Its job: ```text Fundus image ↓ Quality assessment ↓ ┌───────────────┐ │ │ Good Ungradeable │ │ ▼ ▼ DR model Reject ``` Initially, we can even implement a basic rule-based quality check before training a dedicated quality model. Eventually: ```text Quality Model Input image ↓ Quality features ↓ ├── Focus ├── Illumination ├── Field of view └── Overall gradability ↓ Gradeable / Ungradeable ``` This is important because the problem specifically targets variable-quality portable-camera images. --- # 9. One-at-a-time lesion detection The statement asks for: * optic disc/fovea localization * vessel segmentation * microaneurysm detection * exudate segmentation * hemorrhage classification * neovascularization detection ### Phase 1 ```text Fundus → DR severity ``` ### Phase 2 ```text Fundus → DR severity ↘ lesion evidence ``` ### Phase 3 ```text Fundus ├── optic disc/fovea ├── vessels ├── microaneurysms ├── exudates ├── hemorrhages └── neovascularization ``` --- # 10. Explainability Once we have: ```text Image → prediction ``` add: ```text Image ↓ Classifier ↓ Prediction + Grad-CAM ↓ Heatmap ``` The intended question is: > **What part of the retinal image caused the model to make this prediction?** Then eventually correlate those regions with actual lesions. The problem statement explicitly requests Grad-CAM, lesion-level evidence and calibrated confidence. So the eventual output could look conceptually like: ```text DR Grade: 3 Confidence: 91% Referable DR: YES Evidence: ┌──────────────────────┐ │ retinal photograph │ │ + │ │ Grad-CAM │ │ overlay │ └──────────────────────┘ ``` --- # 11. Evaluation For the **5-class problem**, track: ```text Accuracy Precision Recall F1 Confusion matrix ``` For the **referable DR problem**, prioritize: ```text Sensitivity Specificity ROC-AUC PR-AUC ``` The problem statement's explicit targets are: ```text Sensitivity > 90% Specificity > 85% ``` for referable DR. ```text Result -------------------------------- Sensitivity XX.XX% Specificity XX.XX% ROC-AUC X.XXX Accuracy XX.XX% F1 X.XXX ``` --- # 12. First experiment ```text Dataset ↓ Train / validation / test split ↓ Resize + normalize ↓ Pretrained CNN ↓ 5-class classifier ↓ Train ↓ Evaluate ``` Record: ```text Dataset version Number of images Class distribution Image resolution Preprocessing Model architecture Learning rate Batch size Epochs Training time Validation metrics Test metrics ``` --- # 13. Scientific iteration Your experiment progression can be: ```text EXP-001 Baseline CNN ↓ EXP-002 + preprocessing ↓ EXP-003 + augmentation ↓ EXP-004 different backbone ↓ EXP-005 class imbalance handling ↓ EXP-006 hyperparameter tuning ↓ EXP-007 referable-DR optimization ↓ EXP-008 Grad-CAM ↓ EXP-009 quality assessment ↓ EXP-010 integrated pipeline ``` --- # 14. Backlog For the initial skeleton, explicitly put these in the backlog: * Simulink resource optimization * Telemedicine bandwidth simulation * Automated clinical report generation * Full lesion segmentation suite * Neovascularization detection * Ophthalmologist validation interface * Deployment infrastructure * Mobile/web application They're part of the eventual solution, but they're **downstream of the fundamental ML pipeline**. The problem statement ultimately expects the integrated pipeline plus Simulink simulation and benchmark validation. ---