GAN-Enhanced Hybrid Deep Learning with Explainable AI for Automated Cataract Diagnosis

Cataracts, among the most prevalent eye disorders, result in diminished vision due to cloudiness in the eye's natural lens. Timely diagnosis is crucial for preventing irreversible damage. While effective, existing automated systems encounter difficulties like limited dataset variety, lack of interpretability, and suboptimal generalization in real-world scenarios. This study presents a novel deep learning-based method that incorporates Generative AI (GenAI) and Explainable AI (XAI) to enhance cataract detection. The proposed methodology leverages a fine-tuned InceptionResNetV2 with additional layers, trained on a hybrid dataset enriched by merging six open-source datasets, along with synthetic images generated via Generative Adversarial Networks (GANs). Class weights address data imbalance, while stratified K-Fold cross-validation ensures robust evaluation. Our system offers graphical interpretation through Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps, supporting clinical transparency and reliability. The model evaluation reports a mean K-Fold accuracy of 97.58% with a standard deviation of 0.0040, and a 95% confidence interval (CI) of (0.9702, 0.9814). On the external dataset, the model achieved an overall accuracy of 97%, an AUC of 0.9944, and for the cataract class, a precision of 96%, recall (sensitivity) of 94%, F1-score of 95%. Our method, by incorporating synthetic images and explainable AI, ensures enhanced data diversity, addresses class imbalance, reduced dependency on large annotated datasets, and offers greater interpretability that facilitates expert validation and builds stronger clinical trust, making it superior to existing cataract detection systems.