Automatic Kidney Stone Detection using XResNet 152 Model for CT Images

This study provides an automatic kidney stone identification using computed tomography (CT) scan images and cutting-edge deep learning techniques. The identification and categorization of diverse kidney disorders, such as stones and normal structures, are the main goals of this study. Our research intends to transform the diagnosis process by offering an accurate and effective automated system for renal health evaluation using the resilience of the XResNet152 architecture. The methodology includes a well-selected dataset with a variety of kidney diseases, which makes thorough model training, validation and assessment possible. The model used augmentation approaches and preprocessing processes specific to medical imaging data to improve its capacity to identify complex patterns and features that correspond to various kidney abnormalities. The model demonstrated remarkable precision in categorization, demonstrating encouraging outcomes in discerning and differentiating various renal ailments. The model demonstrated remarkable accuracy in distinguishing between stones and normal structures, and it demonstrated resilience in intricate medical imaging situations. In this study, we present a thorough explanation of our approach, dataset preparation, model architecture, and in-depth performance analysis. We critically assess the advantages and disadvantages of our methodology and suggest directions for further development. By advancing the field of medical image analysis, this research opens the door to more advanced precision medicine and better healthcare diagnostics.