IDENTIFICATION OF CHRONIC RENAL ILLNESS USING RENAL STONE PICTURE AND VALUES

Abstract - The escalating global prevalence of kidney stones necessitates innovative methodologies for precise detection and comprehensive functional assessment. This study leverages cutting-edge deep learning techniques, particularly Visual Geometry Group16 (VGG16), to enhance diagnostic accuracy and generalizability. By utilizing a diverse CT image dataset, the proposed model effectively discerns kidney stones while integrating advanced image processing techniques to refine predictive performance. Beyond mere identification, the framework incorporates a multifaceted functional analysis, evaluating critical factors such as obstruction, perfusion dynamics, and tissue integrity, thereby providing deeper clinical insights. The methodological approach encompasses rigorous model training on a meticulously curated dataset, followed by robust validation using key performance metrics, including sensitivity and specificity. The anticipated outcomes of this research include the development of a highly sophisticated AI-driven diagnostic system, fostering early intervention and optimizing patient outcomes. By seamlessly integrating artificial intelligence into renal healthcare, this study aims to redefine clinical paradigms, enhancing both diagnostic precision and therapeutic efficacy Key words - Chronic renal illness-CNN-VGG16-Deep Learning-Image classification