Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Kidney abnormality is one of the major concerns in modern society, and it affects millions people around world. To diagnose different abnormalities human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, used, which creates cross-sectional slices kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification segmentation purposes. However, has difficult clinicians interpret model’s specific decisions and, thus, creating “black box” system. Additionally, integrate complex internet-of-medical-things devices due demanding training parameters memory-resource cost. overcome these issues, this study proposed (1) lightweight customized convolutional neural network detect kidney cysts, stones, tumors (2) understandable AI Shapely values based on Shapley additive explanation predictive results local interpretable model-agnostic explanations illustrate model. The CNN model performed better than other state-of-the-art methods obtained an accuracy 99.52 ± 0.84% K = 10-fold stratified sampling. With improved interpretive power, work provides with conclusive results.