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.

翻译：近年来，威胁视力的眼部疾病发病率急剧上升，亟需可扩展且精准的筛查解决方案。本文针对深度学习架构在眼部疾病自动诊断中的应用进行了系统性研究。为缓解标准卷积神经网络（CNN）的“黑箱”局限性，我们设计了一种结合深度特征提取与可解释图像处理模块的流程。具体而言，我们以高保真视网膜血管分割作为辅助任务来指导分类过程。通过将模型预测建立在临床相关的形态学特征基础上，旨在弥合算法输出与专家医学验证之间的鸿沟，从而降低假阳性率并提升临床场景中的部署可行性。