Automated Urine Sediment Microscopic Image Analysis Using YOLOv10 with Chimpanzee Leader Election Optimization

To diagnose various urinary diseases, it is vital to accurately detect urine cells. This paper presents a novel approach for detecting and classifying urine cells using the YOLOv10 (You Only Look Once version 10) model integrated with the CLEO algorithm. YOLOv10, known for its superior performance in object detection, is combined with CLEO, a nature-inspired optimization algorithm. The model is trained on the Urine Microscopic Image Dataset (UMID), comprising various types of urine cell images. Multiple cell types have been classified, including bacteria, epithelial cells, RBCs, WBCs, crystals, and casts directly from microscopic images of urine samples. Results from rigorous testing show that YOLOv10 integrated with CLEO is a robust tool for analyzing urine sediments, achieving a mean Average Precision@50 (mAP50) of 84% and a recall of 90%. Hence, the proposed approach offers significant speed improvements and consistency as compared to traditional methods of urine sediment analysis. The framework can automatically analyze urine sediment images in real time with good detection performance and has the potential to assist computer-assisted clinical urinalysis, thereby reducing the workload of manual analysis and diagnosis.