Eye disease detection using transfer learning based on retinal fundus image data

Abstract

The escalating global prevalence of blindness remains a pressing concern, with eye diseases representing the primary culprits behind this issue. Vision is integral to various aspects of human life, underscoring the significance of effective eye disease detection. Presently, disease detection relies largely on manual methods, which are susceptible to misdiagnosis. However, the advent of technology has paved the way for disease detection through the application of deep learning methodologies. Deep learning exhibits substantial potential in disease detection, particularly when applied to image data, as attested by its accuracy in algorithmic assessments. This research introduces a novel approach to disease detection, specifically transfer learning-based deep learning. The study seeks to evaluate and compare the performance of various models, including EfficientNetB3, DenseNet-121, VGG-16, and ResNet-152, in identifying three prevalent eye diseases: cataract, diabetic retinopathy, and glaucoma, utilizing retinal fundus image data. Extensive experimentation reveals that the DenseNet-121 model achieves the highest accuracy levels, boasting precision, recall, F1-score, and accuracy values of 96.5%, 96%, 96.25%, and 96.20%, respectively. These results demonstrate the superior performance of the employed transfer learning model, signifying its efficacy in detecting eye diseases.