A deep learning-based study on automated CT diagnosis of kidney stones, hydronephrosis and pyonephrosis.

Abstract

Automated identification of urinary system diseases on non-contrast computed tomography (NCCT) can facilitate early diagnosis and inform treatment decisions. However, kidney stones, hydronephrosis, and pyonephrosis share overlapping NCCT appearances and require diagnostic cues at different spatial scales, making accurate identification within a unified framework challenging. To address this, we constructed an integrated CT-based framework for automated identification of urinary system diseases. First, the collected CT data were split into training, validation, and test sets and processed using a unified preprocessing pipeline for resolution standardization and normalization. Subsequently, we evaluated multiple image enhancement strategies, including histogram equalization, CLAHE, Laplacian sharpening, and brightness enhancement. We conducted a systematic comparison of these enhancement methods to quantify their effects on disease identification performance. To support unified automated diagnosis of kidney stones, hydronephrosis, and pyonephrosis, we developed a multi-disease renal diagnostic network, MSF-TEA Net. Through multi-scale feature fusion and multi-evidence collaborative modeling, it effectively represents the image features of different pathological states. This model introduces Tri-Evidence Attention (TEA), which models the high-density features of small calculi targets, the global morphological changes of fluid accumulation, and the inflammatory texture features of pus accumulation. Through adaptive weighted fusion, the network enhances discrimination and diagnostic performance when complex lesions coexist. Experimental results show that, within the unified MSF-TEA Net framework, Laplacian sharpening achieves the best performance among the tested enhancement methods, with a test accuracy of 94.40% ± 1.13% and an AUC of 99.30%, outperforming the other enhancement strategies. Ablation studies further confirm the contributions of PPM, ASPP, and TEA. Overall, the proposed unified framework enables reliable identification of kidney stones, hydronephrosis, and pyonephrosis, supporting clinical decision-making.