Abstract
Kidney stones are the most common disease, resulting in so many deaths. Early kidney stone detection is essential for minimizing death rates. Early detection and treatment are crucial in the fight against kidney stones. Applying machine learning techniques reduces the workload on physicians while reducing risk and improving diagnostic accuracy. We proposed detection methods in this work that can recognize kidney stones in endoscopy images. For the identification and classification of kidney stones, we suggested five 3D-CNN models. The first three models are used to detect kidney stones; each model has an eight-layer convolutional neural network (CNN-8), while the final two models use a six-layer convolutional neural network (CNN-6) to classify kidney stones. A novel dataset of 1000 images has been collected from various hospitals in Ethiopia. A training set of 0.8 and a testing set of 0.2 were formed from the dataset. The accuracy scores for the 3D-CNN models were 0.985. The novel models produced encouraging outcomes. We think it can address the issues we have had.
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