Authors : Smit Thacker; Ravirajsinh Vaghela

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

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Google Scholar : https://tinyurl.com/2pf3vc8s

Scribd : https://tinyurl.com/ywzbapuu

DOI : https://doi.org/10.38124/ijisrt/26May250

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Chronic Kidney Disease (CKD) often progresses silently until advanced stages, making early detection crucial for timely intervention. This paper introduces KTransNet, a novel hybrid deep learning model that combines Transformerbased encoders with CNN layers and integrates a YOLOv8 detection head for efficient early-stage CKD diagnosis from medical imaging. The model employs a patch-based token embedding strategy with custom positional encoding and dense blocks for feature reuse, enabling robust local and global feature extraction.

Keywords : Chronic Kidney Disease CKD, Medical Imaging, Transformer-CNN Hybrid, Attention Mechanisms.

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