Multi-class Kidney Abnormalities Detecting Novel System through Computer Tomography

Kidney abnormalities encompass a wide spectrum of disorders ranging from benign cysts to malignant tumours, which require timely and accurate diagnosis to enable effective treatment.Computed Tomography (CT) imaging has become a cornerstone in renal pathology detection due to its high resolution and ability to capture detailed anatomical structures.However, manual interpretation of CT scans is prone to variability and time constraints, especially in the presence of multiclass abnormalities.This study proposes a novel, automated system for the detection and classification of multiple kidney abnormalities using advanced image processing and deep learning algorithms.The system utilizes a convolutional neural network (CNN) architecture trained on annotated CT scan datasets to accurately classify kidney conditions into distinct categories, including cysts, stones, tumors, and structural deformities.The model demonstrates high sensitivity and specificity across all classes and integrates a real-time visualization interface for radiologists.Experimental results reveal the superiority of the proposed method in terms of classification accuracy and clinical relevance, showing promise for improved diagnostic workflows in nephrology and radiology practices.