CHRONIC KIDNEY DISEASE DETECTION AND CLASSIFICATION USING DEEP LEARNING METHOD – AN EMPIRICAL PROOF

A significant global health issue that is having an impact on healthcare systems is chronic kidney disease, or CKD. Prediction aids in CKD diagnosis, but the interpretability of these models remains challenging for clinicians. Explainable AI (XAI) plays a critical role in addressing this by offering intelligible insights into model predictions. By using deep learning more specifically, the VGG16 architecture instead of traditional machine learning for the identification and categorization of chronic kidney disease, this work advances earlier investigations. The aim is to enhance diagnostic precision and interpretability. Integrating insights from earlier analysis-driven studies, Grad-CAM analysis is incorporated into the deep learning framework. Providing clear and understandable representations of the neural network's decision-making process in medical image data is the aim of this integration. Medical professionals can more accurately classify and interpret images with greater intelligence when Grad-CAM analysis is integrated to highlight important areas in medical images. The discrepancy between the accuracy and interpretability of CKD diagnosis is closed by this study. Our objective is to improve the quality of healthcare worldwide by providing physicians with a useful tool for precisely identifying chronic kidney disease (CKD) by the integration of deep learning techniques and Grad-CAM insights.