Robust Multi-Disease Retinal Classification via Xception-Based Transfer Learning and W-Net Vessel Segmentation
This work addresses the need for scalable and accurate screening solutions for vision-threatening eye diseases, though it appears incremental by integrating existing methods for interpretability.
The paper tackled the problem of automated diagnosis of ocular conditions by combining deep feature extraction with interpretable image processing, specifically using retinal vessel segmentation to guide classification, aiming to reduce false positives and improve clinical deployment viability.
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.