DEEP LEARNING-DRIVEN RENAL IMAGE CLASSIFICATION: ENHANCING ACCURACY AND EFFICIENCY IN CLINICAL DIAGNOSTICS

Abstract

The precise identification of renal abnormalities, including cysts, calculi, neoplasms, and healthy tissues, is essential for timely intervention and effective management. This paper proposes a unique deep learning strategy for renal image categorization to improve the accuracy and efficiency of diagnostic processes. Conventional approaches for identifying kidney irregularities predominantly depend on manual interpretation of radiological images, which is laborious, subjective, and susceptible to human error. Moreover, these conventional approaches are challenged by the growing amount of medical pictures, necessitating the development of an automated system. Our proposed approach utilizes deep neural networks, particularly convolutional neural networks (CNNs), to autonomously categorize renal pictures into distinct classifications, delivering quick and precise outcomes. The limitations of traditional methods, including inter-observer variability, restricted scalability, and the risk of misdiagnosis, can be substantially alleviated by our deep learning methodology. We trained our algorithm on an extensive dataset of annotated renal pictures, including diverse abnormalities and normal tissues, to guarantee robust performance. Initial findings demonstrate elevated accuracy, sensitivity, and specificity in detecting renal abnormalities, positioning our suggested approach as a valuable asset for enhancing the diagnostic procedure in nephrology.