ML

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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
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

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

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:

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

Image → {0,1,2,3,4}

#Problem B — screening decision

Image → {Non-referable, Referable}

where:

0, 1 → Non-referable
2, 3, 4 → Referable

The problem statement specifically defines Level 2+ as referable.

Our model can therefore produce:

Predicted class: 3
Confidence: 0.87

Referable DR: YES

#6. Model plan

Use transfer learning.

Conceptually:

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

Raw image
   ↓
Resize
   ↓
Crop / remove irrelevant borders
   ↓
Normalize
   ↓
Optional contrast enhancement
   ↓
Model

Later we'll investigate:

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:

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:

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

Fundus → DR severity

#Phase 2

Fundus → DR severity
       ↘
        lesion evidence

#Phase 3

Fundus
 ├── optic disc/fovea
 ├── vessels
 ├── microaneurysms
 ├── exudates
 ├── hemorrhages
 └── neovascularization

#10. Explainability

Once we have:

Image → prediction

add:

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:

DR Grade: 3
Confidence: 91%

Referable DR: YES

Evidence:
 ┌──────────────────────┐
 │ retinal photograph   │
 │       +              │
 │    Grad-CAM          │
 │      overlay         │
 └──────────────────────┘

#11. Evaluation

For the 5-class problem, track:

Accuracy
Precision
Recall
F1
Confusion matrix

For the referable DR problem, prioritize:

Sensitivity
Specificity
ROC-AUC
PR-AUC

The problem statement's explicit targets are:

Sensitivity > 90%
Specificity > 85%

for referable DR.

                    Result
--------------------------------
Sensitivity          XX.XX%
Specificity          XX.XX%
ROC-AUC              X.XXX
Accuracy             XX.XX%
F1                   X.XXX

#12. First experiment

Dataset
   ↓
Train / validation / test split
   ↓
Resize + normalize
   ↓
Pretrained CNN
   ↓
5-class classifier
   ↓
Train
   ↓
Evaluate

Record:

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:

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.