Glaucoma Detection in Retinal Fundus Images

U-Net segmentation of optic cup & disc with automated CDR diagnosis.

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Domain
Computer Vision
Year
2025
Type
Research report — internal engineering work

This is internal engineering and R&D work, not a client project. It is published to show technical depth — the modelling, evaluation, and tooling behind the AI automation we build for clients. No client is associated with it.

Glaucoma is the second-largest cause of blindness worldwide and needs early diagnosis. This project segments the optic cup and optic disc from retinal fundus images using a U-Net model, then derives a Cup-to-Disc Ratio (CDR) to flag likelihood of glaucoma — a system that works without excessive equipment or specialist time.

Tools & methods

  • Python
  • U-Net (encoder/decoder)
  • OpenCV
  • TensorFlow / Keras
Full technical report

Objectives

  • Segment optic cup and optic disc from fundus images.
  • Compute the Cup-to-Disc Ratio for diagnosis.
  • Evaluate with accuracy, precision, recall and IoU.

Process

  • Preprocess: convert images to a uniform (256×256×1) grayscale format; build image, optic-cup and optic-disc lists by thresholding masks.
  • Build a U-Net from conv/encoder/decoder blocks (conv layers + BatchNorm + ReLU, max-pool down, transpose-conv up with skip connections).
  • Train on the dataset (batch size 2, 25–30 epochs).
  • Predict masks, compute CDR; CDR > 0.4 → high likelihood of glaucoma.

Results

  • High segmentation accuracy (optic disc & cup accuracy > 0.98).
  • Automated CDR-based diagnosis with per-task precision/recall/IoU.

What we took from it

  • Consistent preprocessing (fixed input shape) is critical for U-Net.
  • Skip connections preserve spatial detail for fine segmentation.

From the report

  • U-Net architecture for optic cup & disc segmentation
    U-Net architecture for optic cup & disc segmentation
  • Predicted vs original cup/disc masks
    Predicted vs original cup/disc masks
  • Segmentation metrics with predicted masks
    Segmentation metrics with predicted masks
  • Predicted vs original masks (sample 2)
    Predicted vs original masks (sample 2)
  • Predicted vs original masks (sample 3)
    Predicted vs original masks (sample 3)
  • Predicted vs original masks (sample 4)
    Predicted vs original masks (sample 4)

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