Authors : Sajid Ali; Zhang Yihong; Sajad Ul Haq; Ameer Hamza; Md. Saifur Rahman; Nabeel Hussain; Manzar Hussain; Amjad Ali; Irfan Ali

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/yjfr5dvy

Scribd : https://tinyurl.com/ypr9pcah

DOI : https://doi.org/10.38124/ijisrt/26May1422

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Abstract : Early and reliable identification of kidney abnormalities from computed tomography (CT) images is important for supporting clinical decision-making and reducing radiological workload. This study presents a comparative evaluation of transfer-learning-based convolutional neural networks (CNNs) and custom deep learning architectures for CT-based kidney abnormality classification. The publicly accessible CT-Kidney dataset reported by Islam et al. was used, consisting of 12,446 CT images collected from multiple hospitals in Dhaka, Bangladesh. The original dataset contains 5,077 normal kidney images, 3,709 cyst images, 2,283 tumor images, and 1,377 stone images. For this work, the multiclass labels were reorganized into a binary classification task, normal versus abnormal where cyst, tumor, and stone samples were treated as abnormal. Data were divided into training, validation, and testing subsets using a stratified 60:25:15 split to preserve class distribution. Six models were evaluated: VGG16, ResNet50, InceptionV3, InceptionResNetV2, a custom CNN based on ResNet152, and a custom Vision Transformer (ViT). Standard performance metrics including accuracy, precision, recall, F1-score, root mean square error (RMSE), AUC-ROC, and AUC-PR were used for assessment.

Keywords : Computed Tomography, Convolutional Neural Network, Deep Learning, Kidney Disease Classification, Medical Image Analysis, Transfer Learning, Vision Transformer.

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