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

Shashwat Jha; Vishvaditya Luhach; Raju Poddar

eess.IVMar 31, 2026

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

Authors: Shashwat Jha, Vishvaditya Luhach, Raju Poddar

Organizations: Department of Electrical and Electronics Engineering, Birla Institute of Technology Mesra · Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology · Department of Bio-Technology, Birla Institute of Technology Mesra

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.

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RetinaVision: XAI-Driven Augmented Regulation for Precise Retinal Disease Classification using deep learning framework

Mohammad Tahmid Noor, Shayan Abrar, Jannatul Adan Mahi, Md Parvez Mia+2

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Medical Image Classification Image Classification Explainable Medical Image Analysis

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Early and accurate classification of retinal diseases is critical to counter vision loss and for guiding clinical management of retinal diseases. In this study, we proposed a deep learning method for retinal disease classification utilizing optical coherence tomography (OCT) images from the Retinal OCT Image Classification - C8 dataset (comprising 24,000 labeled images spanning eight conditions). Images were resized to 224x224 px and tested on convolutional neural network (CNN) architectures: Xception and InceptionV3. Data augmentation techniques (CutMix, MixUp) were employed to enhance model generalization. Additionally, we applied GradCAM and LIME for interpretability evaluation. We implemented this in a real-world scenario via our web application named RetinaVision. This study found that Xception was the most accurate network (95.25%), followed closely by InceptionV3 (94.82%). These results suggest that deep learning methods allow effective OCT retinal disease classification and highlight the importance of implementing accuracy and interpretability for clinical applications.

Abstract

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