A Novel Transfer Learning Based Kidney Stone Detection Using ResNet50

The kidneys are essential for human health because they maintain vital functions such as blood filtration and electrolyte balance.Detecting kidney stones is a significant medical imperative, requiring precise diagnosis to ensure timely intervention.Healthcare personnel must manually review medical photographs as part of traditional diagnostic procedures, which can be laborious and prone to human error.A novel method for the automated detection of kidney stones based on the analysis of coronal computed tomography (CT) images has been proposed.This method leverages recent advancements in artificial intelligence to achieve accurate identification of the occurrence or non-occurrence of kidney stones.In our proposed system, we address the task of kidney stone detection through transfer learning (TL) with ResNet50 using medical images.The model demonstrate to address limited medical data challenges, superior accuracy and sensitivity in detecting kidney stones compared to traditional methods.Moreover, it exhibit robust ability to generalize, even in the presence of variations and noise in medical images.