Hybrid U-Net Architectures with ResNet50 and VGG19 for Accurate CT-Based Kidney Disease and Stone Segmentation
DOI:
https://doi.org/10.21928/uhdjst.v9n2y2025.pp231-250Keywords:
Kidney stone, Renal disorders, DL, U-Net, VGG19, ResNet50Abstract
Kidney illness is a major worldwide health issue requiring prompt and precise diagnosis for optimal management. This paper presents a comprehensive evaluation of hybrid deep learning (DL) architectures that integrate U-Net with ResNet50 and VGG19 for the automatic segmentation of kidney stones and renal disorders from computed tomography (CT) images. We assembled a dataset of 118 individuals from a private hospital, comprising 13,035 kidney-specific CT scans, while also using the publicly accessible Kaggle Kidney Stone Segmentation Dataset. Three experimental situations were established: (1) Concurrent segmentation of kidney disease and stones, (2) segmentation of kidney stones alone, and (3) segmentation of kidney disease exclusively. The hybrid U-Net+ResNet50 model attained superior performance in stone-only segmentation, with an F1-score of 0.8653, an IoU of 0.7626, and an accuracy of 0.9998 at a resolution of 256 × 256. The U-Net+VGG19 model exhibited strong performance in all situations, attaining an F1-score and DC of 0.8663 for stone segmentation. Both models demonstrated exceptional generalization ability when evaluated on external datasets. The findings indicate that hybrid architectures markedly improve segmentation accuracy compared to conventional methods, providing dependable automated tools for clinical kidney pathology evaluation while ensuring computational efficiency with average processing durations below 0.05 s per scan.
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Copyright (c) 2025 Dlshad Abdalrahman Mahmood, Muhammad Amin Muhammadali
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