Enhancing Retinal Scan Classification: A Comparative Study of Transfer Learning and Ensemble Techniques

Abstract

Ophthalmic diseases are a significant health concern globally, causing visual impairment and blindness in millions of people, particularly in dispersed populations. Among these diseases, retinal fundus diseases are a leading cause of irreversible vision loss, and early diagnosis and treatment can prevent this outcome. Retinal fundus scans have become an indispensable tool for doctors to diagnose multiple ocular diseases simultaneously. In this paper, the results of a variety of deep learning models (DenseNet-201, ResNet125V2, XceptionNet, EfficientNet-B7, MobileNetV2, and EfficientNetV2M) and ensemble learning approaches are presented, which can accurately detect 20 common fundus diseases by analyzing retinal fundus scan images. The proposed model is able to achieve a remarkable accuracy of 96.98% for risk classification and 76.92% for multi-disease detection, demonstrating its potential for use in clinical settings. By utilizing the proposed model, doctors can provide swift and accurate diagnoses to patients, improving their chances of receiving timely treatment and preserving their vision.