Redefining Cataract Diagnosis: Leveraging MobileNetV2-Based CNN for Automated Detection from Fundus Images

Abstract

Cataracts remain a prevalent cause of global blindness, emphasizing the urgent need for timely and accurate diagnostic solutions. This study introduces an innovative automated cataract detection method using fundus images, employing three distinct convolutional neural network architectures: MobileNetV2, EfficientNetB0, and ResNet50. Through transfer learning, these types of models were developed using a dataset containing both cataract and non-cataract fundus images. The outcomes show how successful the suggested models were in precisely classifying fundus images, with notable performance indicators including F1-score, recall, accuracy, and precision. The MobileNetV2-based model achieved 98% accuracy, EfficientNetB0 attained 68% accuracy, while the ResNet50 model, enhanced with additional dense layers, attained 69% accuracy. Furthermore, comprehensive evaluation including classification reports and confusion matrices validates the robustness and generalization capabilities of the models, confirmed through cross-validation on independent test sets. This convolutional neural network-based approach holds promise for scalable, cost-effective automated cataract detection in clinical settings, with the potential for further advancements in model interpretability and dataset diversification to enhance its applicability in diverse populations.