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

TL;DR

This paper presents RetinaVision, a deep learning framework utilizing CNNs and interpretability tools for accurate retinal disease classification from OCT images, demonstrating high accuracy and clinical relevance.

Contribution

Introduces RetinaVision, combining CNN-based classification with interpretability methods for retinal diseases, and demonstrates its effectiveness on a large OCT dataset.

Findings

Xception achieved 95.25% accuracy

InceptionV3 achieved 94.82% accuracy

Interpretability tools like GradCAM and LIME enhance clinical trust

Abstract

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.