An Efficient System for Identification of Eye Disease in Fundus Images using a Deep Transfer Learning-based Pre-trained Model

Ophthalmologists rely heavily on retinal fundus imaging to diagnose retinal diseases. Early detection can enhance the likelihood of a cure and also prevent blindness. Retinal fundus images can be used by medical professionals to diagnose retinal conditions such as diabetic retinopathy and retinitis pigmentosa. This study proposes an automated diagnostic approach using a Deep Learning (DL) model to identify fundus images with a high prediction rate. This study aims to use multilabel classification to identify diseases in fundus images. An EfficientNet-B5-based model was trained on a fundus image dataset to classify images as normal, NPDR, and PDR. Image preprocessing was used, including conversion to RGB format, resizing to 224×224, and image filtering using the Gaussian blur algorithm. Additionally, 10-fold cross-validation was used to train and validate the proposed approach. The enhanced EfficientNet-B5 model demonstrated superior validation and training accuracy for eye disease classification compared to existing techniques, achieving 96.04% and 99.54%, respectively. This technology enables early detection and treatment of eye conditions, potentially improving patient outcomes.