Kidney Stone Detection Using Ultrasonographic Images by Support Vector Machine Classification

Abstract

Kidney stones represent a prevalent and often painful urological condition. Timely and precise diagnosis is pivotal for effective management. This research investigates the application of advanced image-processing techniques to augment the identification of kidney stones in ultrasonographic images. The devised methodology encompasses three main phases: preprocessing, feature extraction, and classification. Through this multifaceted approach, we seek to enhance the accuracy and efficiency of kidney stone detection. The initial results from our research exhibit notable improvements in kidney stone identification compared to traditional manual methods. By utilizing machine learning method, such as support vector machines, we achieved a promising accuracy rate in the automated classification of kidney stones based on their size, composition, and location. Moreover, our system, when integrated with Electronic Health Records (EHR), ensures comprehensive and accessible patient data, ultimately empowering healthcare professionals to make informed decisions about diagnosis and treatment. In order to improve patient care standards and optimize healthcare procedures, this research offers a novel step toward more accurate kidney stone detection.