Streamlining Kidney Stone Detection through Image Processing and Deep Learning

In this paper an automated system for precise kidney stone identification in computed tomography CT scans is introduced. The system consists of two essential components custom CNN for image classification and fuzzy c-means FCM clustering for localizing stones, the CNN architecture is trained to classify kidney CT scans as normal or abnormal scans based on a dataset collected from Kaggle. Subsequently FCM clustering is then applied on the abnormal images to automatically detect and localize kidney stones by segmenting pixels of the same intensity. This computer-assisted method applying machine learning-based image processing should yield better accuracy over traditional manual techniques like thresholding filtering and edge detection. By automating the detection process this system aims to provide radiologists and urologists with an effective tool for rapid and accurate identification of kidney stones enabling effective and timely patient care. The project is simulated and implemented on MATLAB software.