Clinically Oriented Deep Learning Framework for Multi-Class Kidney Abnormality Instance Segmentation in CT Images

This study presents a clinically oriented deep learning framework for multi-class kidney abnormality instance segmentation in Computed Tomography (CT) images using the YOLOv12 architecture. A comprehensive and class-balanced dataset of 14,761 axial and coronal CT scans was curated, preprocessed, and augmented to support robust model training and generalization across cyst, stone, and tumor cases. The YOLOv12 models were trained and evaluated separately for axial and coronal orientations, demonstrating strong performance in both views, with mAP@0.5 scores of 0.946 and 0.885, respectively. Training analysis showed rapid convergence in classification tasks, nearly identical segmentation performance across…