Analysis of Urine Sediment Images for Detection and Classification of Cells

Urine sediment tests are important in diagnosing abnormal diseases related to the urinary tract. The formation of cells such as red blood cells and white blood cells in the urine of patients is important for diagnosing the disease. Therefore, cells need to be fully identified in clinical urinalysis. Urinalysis with human eyes; since it is subjective, time consuming and causing errors, methods have been developed to automate microscopic analysis with the help of image processing. In this study, a deep learning algorithm (Yolov7), which gives successful results in image processing technology, was used as a method. The dataset used in the study was created by using microscopic images of urine sediment taken from the Biochemistry Laboratory of the Faculty of Medicine, Selcuk University. Seven different cell segmentation and classification studies have been carried out, including WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles, which have clinical value for diagnosing the disease. Experimental studies were carried out with the Yolov7 algorithm, and the results were presented. As a result of the experiment, the urine cell images were segmented into cells using the deep learning method. The segmentation performance metrics, precision, recall, mAP(0.5) and F1-Score(%) were calculated as 0.384, 0.759, 0.432 and 0.510, respectively. The segmented cells were classified as WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles and the classification accuracies were obtained as 0.78, 0.94, 0.90, 0.57, 0.92, 0.68 and 0.97, respectively. A mean classification success of 0.822 was achieved for all classes. Thus, it has been seen that the Yolov7 model can be used by experts as a tool for recognizing cells in the urine sediment. Consequently, it has been shown that suitable deep-learning models can be used to recognize the biometric properties of urinary sediment cells. With the model created using deep learning libraries, urine sediment cells can be easily classified, and it is possible to define many different cells if there is a dataset with a sufficient number of images.