Demystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI

TL;DR

This paper introduces a smaller, simpler CNN model for retinal OCT disease classification that incorporates Explainable AI via Lime to improve interpretability, trust, and efficiency in clinical settings.

Contribution

A novel, lightweight CNN model combined with Lime explainability enhances interpretability and trustworthiness in retinal OCT disease classification.

Findings

The proposed CNN model is smaller and faster than existing models.

Explainability via Lime improves understanding of model decisions.

The approach supports real-time clinical application.

Abstract

In the world of medical diagnostics, the adoption of various deep learning techniques is quite common as well as effective, and its statement is equally true when it comes to implementing it into the retina Optical Coherence Tomography (OCT) sector, but (i)These techniques have the black box characteristics that prevent the medical professionals to completely trust the results generated from them (ii)Lack of precision of these methods restricts their implementation in clinical and complex cases (iii)The existing works and models on the OCT classification are substantially large and complicated and they require a considerable amount of memory and computational power, reducing the quality of classifiers in real-time applications. To meet these problems, in this paper a self-developed CNN model has been proposed which is comparatively smaller and simpler along with the use of Lime that introduces Explainable AI to the study and helps to increase the interpretability of the model. This addition will be an asset to the medical experts for getting major and detailed information and will help them in making final decisions and will also reduce the opacity and vulnerability of the conventional deep learning models.