Deep Learning-Based Detection of Renal Abnormalities from CT Images: A Study on a Bangladeshi Cohort

Abstract

This study presents an AI-driven computed tomography (CT) diagnostic system for the automated detection and classification of renal abnormalities in the Bangladeshi population. Renal abnormalities such as kidney cysts, stones, and tumors require timely and accurate diagnosis to reduce complications and improve treatment outcomes. In Bangladesh, the increasing burden of kidney-related diseases has created a strong need for efficient and intelligent diagnostic support systems. To assess the effectiveness of deep learning in multiclass renal abnormality diagnosis, three advanced convolutional neural network architectures were implemented and compared: Xception, VGG16, and ResNet152V2. The experimental results demonstrated outstanding classification performance across all models, with Xception achieving the highest accuracy of 99.84%, followed by ResNet152V2 at 99.76%, and VGG16 at 99.40%. Among the evaluated approaches, Xception showed the best overall performance, indicating its strong capability for reliable renal abnormality classification from CT images. The proposed system has significant potential to assist radiologists and healthcare professionals by providing fast, accurate, and automated diagnostic support. This work highlights the promise of artificial intelligence in medical imaging and contributes to the advancement of intelligent diagnostic solutions for kidney disease detection in Bangladesh.