A Deep Learning-Based Diabetic Retinopathy Classification and Diagnosis System

Diabetic Retinopathy (DR) was a chronic complication caused by diabetes. If not diagnosed and treated in time, it can severely affect vision and even lead to blindness. Due to the diversity and complexity of lesion areas, relying solely on manual detection make it difficult to establish quantitative judgment standards and introduces significant uncertainty. Therefore, developing an efficient automated diagnostic system was crucial. Thesis proposed a diabetic retinopathy classification and diagnosis system based on the PyQt5 platform. The system was trained using the APTOS2019 dataset and emploied a method combining Residual Networks (ResNet) with Focal Loss. This approach enabled the system to autonomously extract features from lesion areas and accurately classify the severity of the lesions. This not only helped to conserve medical resources and improve diagnostic efficiency but also provided reliable decision support for clinicians, thereby improving patient treatment outcomes.