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Diabetes Retinopathy, a leading cause of vision impairment, necessitates early and pre- cise detection. To address this, we developed a Convolutional Neural Network (CNN) model and tuned three popular pre-trained models, namely VGG16, Xception, and Mo- bileNetV2, to suit the specific characteristics of our dataset. To better understand the functioning of these deep learning algorithms, Explainable AI (XAI) techniques, such as CAM and Grad CAM++, were employed to highlight the crucial features influencing the model’s classifications. This study extends to the realm of imaging analysis, emphasiz- ing the critical importance of carefully selecting and customizing models to ensure precise and dependable diagnosis of complex conditions such as DR. Notably, the VGG16 model exhibited strong performance in identifying cases categorized as ’Moderate’ and ’No DR’, achieving accuracies of 0.90 and 0.98, respectively. Similarly, both Xception and Mo- bileNetV2 demonstrated promising results in the DR categories. Remarkably, our custom CNN model, tailored for our dataset, achieved an accuracy of 0.986 in identifying cases without DR (’No DR’). These results underscore the effectiveness of the trained deep learn- ing models in accurately diagnosing DR.
Mridha et al. (Fri,) studied this question.