Kidney Stone Detection Using Advanced Deep Neural Networks

Chronic kidney disease (CKD) is a widespread medical issue that results from diminished kidney function and, in severe cases, leads to kidney failure. One of the contributing factors to impaired kidney performance is the development of kidney stones. Since this condition often shows no noticeable symptoms during its early stages, timely and accurate diagnosis is crucial to preventing serious complications. In this research, we propose a highly effective and reliable method for detecting kidney stones by leveraging ensemble deep learning models enhanced through inductive transfer learning. The methodology incorporates data from two main sources: the Kidney Data and the CT Kidney Stone datasets. For classification purposes, a variety of deep learning architectures were utilized, including DarkNet19, InceptionV3, ResNet101, DenseNet169, MobileNetV2, VGG16, GoogleNet, AlexNet, ShuffleNet, SqueezeNet, and a custom-designed DNN model (FindWell). These models were further supported by feature extraction and selection processes using the ReliefF algorithm. Classification accuracy was validated through K-Nearest Neighbors (KNN) and K-Fold cross-validation techniques. For the detection task, different models from the YOLO (You Only Look Once) family—namely YOLO v5x6, v5s6, v8n, and v9n—were deployed to identify kidney stones in imaging data. In addition, the Xception architecture was applied for a comprehensive analysis of the dataset. This integrated approach of combining multiple cutting-edge algorithms enhances both the precision and speed of kidney stone identification, which can significantly aid in the early diagnosis and treatment of patients suffering from chronic kidney-related disorders.