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The utilization of image processing and computer vision technologies has shown remarkable efficacy in analyzing kidney ultrasound images for the recognition of kidney stone related problems. The motivation for this research is the increasing prevalence of kidney disease worldwide, which is a serious health issue. The study focuses on kidney stones, a common and painful condition requiring timely and accurate diagnosis. Ultrasound imaging, preferred for its non-invasive, radiation-free, and cost-effective nature, often suffers from speckle noise and low contrast, complicating the accurate identification and classification of kidney stones. This underscores the need for enhanced image processing techniques to improve ultrasound image quality and diagnostic value. By adopting a comprehensive approach that includes image acquisition, pre-processing, feature extraction, and classification, the research aims to develop an automatic system for reliable and accurate kidney stone detection. The ultimate goal is to create a robust computer-aided detection system that aids medical professionals by reducing diagnostic burden, minimizing errors and enhancing the efficiency of stone detection.