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

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.