Deep Learning Models for Kidney Stone Detection and Risk Assessment

The diagnosis of kidney stones continues to be a substantial obstacle in the healthcare sector, necessitating the development of machine learning (ML) and deep learning (DL) techniques for medical image analysis. This investigation assesses the efficiency and computational efficiency of an enhanced deep neural network (DNN) architecture in comparison to conventional convolutional neural networks (CNNs) and transfer learning methodologies. The proposed methodology’s objective is to improve the early detection and personalized treatment of kidney stone patients by resolving dataset constraints, refining hyperparameter tuning, and providing computational insights.