Abstract

This study investigates the use of deep learning models to forecast the development of kidney stones. We perform extensive preparation using a Kaggle dataset in order to get the data ready for analysis. We separately apply the ResNet and SENet designs, leveraging the residual learning of ResNet and the squeeze-and-excitation method of SENet, to determine their effectiveness in collecting complex patterns suggestive of the presence of kidney stones. By means of comprehensive training and assessment, we measure the predicted precision of every model. In the context of kidney stone prediction, a comparison of the ResNet and SENet designs highlights their distinct advantages and disadvantages. Our results not only shed light on the differences in performance between various architectures but also highlight the potential of deep learning methods to improve kidney stone prediction-related medical diagnostics. This study offers insightful information that can guide the creation of prediction algorithms for kidney stone identification that are more precise and effective, improving patient outcomes and healthcare procedures.