Robust Multi-Disease Retinal Classification via Xception-Based Transfer Learning and W-Net Vessel Segmentation

TL;DR

This paper introduces a robust retinal disease classification method that combines transfer learning with vessel segmentation to enhance interpretability and accuracy in ocular diagnostics.

Contribution

It proposes a novel pipeline integrating Xception-based transfer learning with W-Net vessel segmentation to improve disease classification and interpretability in retinal images.

Findings

Enhanced classification accuracy over baseline models

Improved interpretability through vessel segmentation

Reduced false positives in disease detection

Abstract

In recent years, the incidence of vision-threatening eye diseases has risen dramatically, necessitating scalable and accurate screening solutions. This paper presents a comprehensive study on deep learning architectures for the automated diagnosis of ocular conditions. To mitigate the "black-box" limitations of standard convolutional neural networks (CNNs), we implement a pipeline that combines deep feature extraction with interpretable image processing modules. Specifically, we focus on high-fidelity retinal vessel segmentation as an auxiliary task to guide the classification process. By grounding the model's predictions in clinically relevant morphological features, we aim to bridge the gap between algorithmic output and expert medical validation, thereby reducing false positives and improving deployment viability in clinical settings.