Renal calculi, often known as kidney stones, are solid masses made mostly of crystals. Detecting the perfect and correct site of urinary calculus is essential for surgical procedures. Because CT pictures include greater speckle noise, manually detecting urinary calculi is difficult, hence automated systems for detecting kidney stones in CT images are necessary. CT imaging is one of the imaging modalities available for diagnosing kidney abnormalities, which can include changes in shape and position, as well as swelling of the limb; other kidney abnormalities include the creation of stones, cysts, urine obstruction, congenital defects, and malignant cells. This research presents a new method for detecting kidney stones. This project is divided into two parts: kidney CT image classification and kidney stone detection. For renal CT image classification, VGG16 convolutional neural networks are employed, and for kidney stone identification, Fuzzy c means clustering is used. Filtering and processing of kidney CT scans removes undesired noise and enhances the image. Using complicated techniques such as the Convolutional Neural Network (CNN) model, classify the image using the SoftMax classifier algorithm, and ultimately detect the kidney stone using FCM. This technology will produce more accurate findings and will do it faster than the previous way.

Keywords : Kidney Stones, Image Processing, CT image, CNN.