Enhancing Diagnosis of Kidney Ailments from CT Scan with Explainable AI

In medical informatics, diagnosing, progression of diseases, and detecting kidney organ abnormalities has long posed significant challenges. However, a novel approach has emerged, harnessing the power of AI Shapley values to unravel the decision-making processes of diagnostic models. This innovative framework seamlessly integrates ResNeXt and XAI models, outperforming existing methods and achieving remarkable accuracy rates of 99.52%. The method leverages a robust validation technique to attain this level of precision. Notably, the incorporation of Shapley values within this framework enhances the transparency of decision-making, elevating diagnostic precision and instilling confidence in treatment decisions. The successful integration of ResNeXt and XAI models into a handheld device platform hints at the potential to democratize advanced diagnostics, particularly in resource-constrained settings, making cutting-edge diagnostic technologies accessible to a broader population. This research marks a significant milestone in kidney abnormality diagnosis, promising improved patient care, and better outcomes for kidney health. Furthermore, the proposed architecture sets a precedent for integrating intricate deep learning models into medical handheld devices. Implementing this approach offers a dependable and effective diagnostic tool for the early identification of kidney abnormalities, paving the way for timely interventions and enhanced patient outcomes. The findings from this study underscore the transformative potential of AI-powered medical diagnostics, particularly in kidney disease detection and treatment.