AI / ML · Research

Eye Disease Detection

A peer-reviewed study on classifying eye diseases from retinal imagery using transfer learning. We compared three convolutional architectures, VGG19, EfficientNetB0, and MobileNetV2, with accessible, low-cost medical screening as the goal.

Google Scholar

Role: ML research & engineering
Client: Journal project
Timeline: Research project
Year: 2025

Transfer learning across VGG19, EfficientNetB0, and MobileNetV2

Focused on accessible, low-cost screening

Peer-reviewed and indexed on Google Scholar

Overview

Published as a peer-reviewed paper with co-authors, this project investigates how well modern convolutional networks can classify eye diseases directly from retinal images. The motivation is practical: where a specialist is not nearby, a reliable screening model can help triage who needs urgent care.

The challenge

Medical imaging datasets are small, imbalanced, and noisy compared with the datasets these networks were originally trained on. Training from scratch would overfit, so the real question was how far transfer learning could carry compact, deployable models.

Approach

I built a pipeline around transfer learning, fine-tuning VGG19, EfficientNetB0, and MobileNetV2 on the retinal dataset and comparing them across accuracy and generalisation. The work covered the full loop: preprocessing and augmentation, training, and evaluation, with care taken not to over-read a single metric.

Outcome

The comparison gives a clear, honest picture of the trade-offs between a heavy classic architecture and leaner, efficiency-first ones for this task, the grounding needed before anything approaches real screening use. The paper is peer-reviewed and available on Google Scholar.