YOLOv5 based detector for eight different urine particles components on single board computer

Abstract

Urine sediment examination (USE) is an important issue in the detection of urinary system diseases and is a prerequisite, especially in the diagnosis of kidney diseases. The low-contrast urine sediment images contain many particles, and these particles are superimposed or adherent, making it difficult to detect and interpret the particles. In this paper, a YOLOv5-based urine analysis system, which is one of the fastest and most successful architectures used for object detection, is presented to identify particles in urine. The artificial intelligence-based system, which can recognize bacteria, leukocytes, erythrocytes, crystals, yeast, cylinders, epithelium, and other particles, provides counting and reporting of components in the images obtained from the centrifuged urine sample through a microscope. The system consists of taking images from the microscope and recording them on SBC (Single Board Computer), artificial intelligence-based software running on SBC, and database systems where urine analysis results are recorded. Different YOLOv5x architectures were used to evaluate the performance of the system and Precision, Recall, mAP, and F1_score values were obtained as metrics. According to the results obtained with the YOLOv5n, YOLOv5s, and YOLOv5m architectures, the highest mAP value of the system, which can recognize eight different particles, was 95.8% with YOLOv5m. This artificial intelligence-based system, which will help laboratory workers, will not only save time but also eliminate the standardization differences in manual microscopy and will provide benefits as full-time educational material.