An Accurate Urine Red Blood Cell Detection Method Based on Multi-Focus Video Fusion and Deep Learning with Application to Diabetic Nephropathy Diagnosis

Abstract

Background and Objective: Detecting urine red blood cells (U-RBCs) is an important operation in diagnosing nephropathy. Existing U-RBC detection methods usually employ single-focus images to implement such tasks, which inevitably results in false positives and missed detections due to the abundance of defocused U-RBCs in the single-focus images. Meanwhile, the current diabetic nephropathy diagnosis methods heavily rely on artificially setting a threshold to detect the U-RBC proportion, whose accuracy and robustness are still supposed to be improved. Methods: To overcome these limitations, a novel multi-focus video dataset in which the typical shape of all U-RBCs can be captured in one frame is constructed, and an accurate U-RBC detection method based on multi-focus video fusion (D-MVF) is presented. The proposed D-MVF method consists of multi-focus video fusion and detection stages. In the fusion stage, D-MVF first uses the frame-difference data of multi-focus video to separate the U-RBCs from the background. Then, a new key frame extraction method based on the three metrics of information entropy, edge gradient, and intensity contrast is proposed. This method is responsible for extracting the typical shapes of U-RBCs and fusing them into a single image. In the detection stage, D-MVF utilizes the high-performance deep learning model YOLOv4 to rapidly and accurately detect U-RBCs based on the fused image. In addition, based on U-RBC detection results from D-MVF, this paper applies the K-nearest neighbor (KNN) method to replace artificial threshold setting for achieving more accurate diabetic nephropathy diagnosis. Results: A series of controlled experiments are conducted on the self-constructed dataset containing 887 multi-focus videos, and the experimental results show that the proposed D-MVF obtains a satisfactory mean average precision (mAP) of 0.915, which is significantly higher than that of the existing method based on single-focus images (0.700). Meanwhile, the diabetic nephropathy diagnosis accuracy and specificity of KNN reach 0.781 and 0.793, respectively, which significantly exceed the traditional threshold method (0.719 and 0.759). Conclusions: The research in this paper intelligently assists microscopists to complete U-RBC detection and diabetic nephropathy diagnosis. Therefore, the work load of microscopists can be effectively relieved, and the urine test demands of nephrotic patients can be met.

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

“…As a result, the mAP value was 0.915. 15 Mondal et al developed a smartphone-based prediction application with the YOLOv3 deep learning algorithm to accurately detect and predict glucose/bromide ion concentrations. 16 The urine sample was taken for examination within the first 1 h. After the samples were accepted in the laboratory, they were centrifuged at 1500 rpm (400-450 g) for 5 min with BD brand 10 mL urine tubes.…”

“…The method uses the high‐performance deep learning model YOLOv4 to quickly and accurately detect RBCs based on the D‐MVF, merged image, at the detection stage. As a result, the mAP value was 0.915 15 . Mondal et al developed a smartphone‐based prediction application with the YOLOv3 deep learning algorithm to accurately detect and predict glucose/bromide ion concentrations 16 .…”

An Optimized Data and Model Centric Approach for Multi-Class Automated Urine Sediment Classification

“…This examination entails the analysis of urinary cell morphology, both cellular and noncellular casts, the enumeration of white blood cells (WBCs) and red blood cells (RBCs), and the identification of endogenous crystals. These parameters hold paramount importance in the assessment of various acute or chronic medical conditions [1], [2], [3], [4]. Moreover, the identification and measurement of protein or sugar in urine serve as crucial indicators in diagnosing several kidneyrelated ailments, particularly kidney failure [5].…”