High‐precision real‐time urine crystallization recognition based on dilated bilinear space pyramid ConvNext

Abstract

AbstractUrinary sediment image detection, as one of the three major routine clinical tests in medical practice, is an important method for physical examination and diagnosis of urinary system diseases. Crystalluria detection is a subtask of urinary sediment image detection, focusing on detecting and identifying crystalline components in urine. To address the issues of low accuracy and inefficiency caused by small crystal granularity in crystalluria detection, we propose the Dilated Bilinear Space Pyramid ConvNext Network (DBSPC‐Net), which achieves high‐precision real‐time crystalluria detection. DBSPC‐Net ingeniously combines dilated convolution pooling with bilinear space pyramid, introducing Dilated Bilinear Space Pyramid Pooling (DBSPP) to enlarge the receptive field and capture information at multiple scales. Additionally, we utilize the Normalized Gaussian Wasserstein Distance Loss (NWDLoss) instead of Intersection over Union (IoU) to enhance the recognition of small targets. Finally, the ConvNext module is employed to fuse local and global features, enhancing urine crystal recognition accuracy and speed. The crystalluria dataset is sourced from 400 actual patients in a hospital. It comprises five main types of urine crystals, namely calcium oxalate dihydrate, calcium oxalate monohydrate, uric acid, ammonium magnesium phosphate, and cystine. Experimental results demonstrate that the proposed improved model achieves an average precision of 87.34% and a detection time of 7.9 ms per urine crystal image. DBSPC‐Net can accurately and rapidly identify crystalluria objects in scenarios involving microscope mica compensation, meeting the requirements of algorithmic detection accuracy and real‐time performance in crystalluria detection.