Retinal Malady Classification using AI: A novel ViT-SVM combination architecture

TL;DR

This paper proposes a hybrid ViT-SVM architecture to classify OCT scans for early detection of retinal diseases like Macular Holes, Central serous retinopathy, and Diabetic Retinopathy.

Contribution

It introduces a novel combination of Vision Transformer and Support Vector Machine for retinal disease classification from OCT scans.

Findings

ViT-SVM achieves high accuracy in classifying retinal diseases.

The hybrid model outperforms traditional single-model approaches.

Early detection potential demonstrated through classification results.

Abstract

Macular Holes, Central serous retinopathy and Diabetic Retinopathy are one of the most widespread maladies of the eyes responsible for either partial or complete vision loss, thus making it clear that early detection of the mentioned defects is detrimental for the well-being of the patient. This study intends to introduce the application of Vision Transformer and Support Vector Machine based hybrid architecture (ViT-SVM) and analyse its performance to classify the optical coherence topography (OCT) Scans with the intention to automate the early detection of these retinal defects.