Abstract
Abstract: Kidney stones play a role in the development of chronic kidney disease. Recurrent kidney stones should be avoided not only because of their immediate clinical manifestations but also because of their long-term predisposition to CKD progression. A lot of people confess to emergency departments with excruciating pain due to kidney stones, which are prevalent ailments around the world. The diagnosis of kidney stone illness involves the use of many imaging modalities. For the entire diagnosis and interpretation of these photos, specialists are required. Systems for computer-aided diagnosis are useful methods that can be utilized as supplemental tools to aid clinicians in their diagnosis. The deep learning (DL) technique, which has lately achieved considerable advancements in the field of artificial intelligence, is offered in this work as a means of automating the detection of kidney stones (containing stones or not) using coronal computed tomography (CT) scans. Different cross-sectional CT images were taken for every individual, resulting in a total of 1453 images. Using CT scans, we have seen that even little kidney stones are accurately detected by our model. This study demonstrates that other difficult urological problems can be addressed using newly popular DL approaches
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