Analysis of Urine Sediment Images for Detection and Classification of Cells

Abstract

Urine sediment tests are important in the diagnosis of 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 the diagnosis of 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 computer and software systems. In this study, the YOLO-v7 algorithm, which gives successful results in image processing technology, was used as a method and model. 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 the diagnosis of the disease. Experimental studies were carried out with the YOLO-v7 algorithm and the results were presented. The contributions of this study can be summarized as follows. (1) In this study, which is proposed for segmentation of cells on the urine cell images in the Urine Sediment dataset, for the experimental studies carried out with the YOLO model, whose performance was evaluated; Precision, Recall, mAP(0.5) and F1-Score(%) segmentation performance metrics were calculated as 0.384, 0.759, 0.432 and 0.510, respectively. (2) A computer-aided support system to assist physicians in segmenting urine cells is presented as a secondary tool. Classification accuracy for WBC, RBC, WBCC, Epithelial, Flat Epithelial, Mucs and Bubbles cells was calculated 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. As a result, 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 sufficient number of images.

Keywords

• deep learning
• urine sediment
• classification
• segmentation