Explainable AI for Diabetic Retinopathy Detection Based on a Hybrid-stacked Model

Abstract

Diabetes is among the major threats to human health, which is still incurable despite significant scientific and medical advances. There are ways to identify its presence before it seriously harms the body because it affects all bodily parts and organs. Diabetes also affects the retina of the eyes, rupturing blood vessels there and ultimately leading to irreversible blindness due to complications. This study suggests an enhanced activation function for diagnosing DR using fundus images automatically lowers processing time and loss. In this work, the proposed system design is constructed using a stacking-based Explainable AI model. The increased activation process within the various CNN models was trained and tested using the “Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection” (APTOS 2019) dataset. The pre-processing phase following data collection revealed data segmentation and augmentation. The deep learning prediction approach is used in the recommended work. Robust stacked model is generated by applying CNN, VGG, and custom-versed model methods. The classification of DR and the operation of explainable AI algorithms are explained in this recommended section. The LIME and SHAP approaches shape the stages of doctoral education. Based on the profile-rated, moderate, and severe phases of DR, the suggested figure illustrates the procedures. The accuracy of the current model was 76.78%. The study contributes by proposing a novel hybrid-stacked model architecture tailored for diabetic retinopathy detection. The future scope for this study could be the integration of additional explain ability techniques such as attention mechanisms or saliency maps to provide more detailed insights into the model's decision-making process.