Fundus Image-Based Assisted Diagnosis Using Deep Learning Technique

Abstract

Existing fundus image classification approaches mostly focus on grading a single type of ocular disease, while many deep neural networks still suffer from large parameter sizes and high computational cost. To address these issues, this work develops a multi-label fundus image assisted diagnosis system built on the lightweight SqueezeNet architecture. The model is trained and evaluated on the public ODIR dataset, and the results demonstrate that the proposed system exhibits strong feature extraction capability. It significantly accelerates inference and reduces memory consumption without causing substantial performance degradation. Owing to its compact structure and high efficiency, the system is well suited for deployment on resource-constrained hardware platforms and provides useful insights for integrating artificial intelligence into computer-aided medical diagnostic devices.