Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning

Eye diseases such as glaucoma, cataract, and diabetic retinopathy pose significant global health challenges, underscoring the need for accurate and efficient diagnostic systems. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the Ocular Disease Intelligent Recognition (ODIR) dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation—an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. The proposed approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making.