Abstract: The procedure of counting various blood cells from a smear image will be substantially facilitated by an automated method. Applications for object detection and picture classification are improving in accuracy thanks to the development of machine learning algorithms. the approach for detecting various blood cells based on machine learning. You only need to look once when using cutting-edge object detection techniques like regions with convolutional neural network (R-CNN) (YOLOV3). In one evaluation, YOLOV3 employs a single neural network to forecast bounding boxes and class probabilities based on the entire image. Additionally, photos are annotated with the labelling tool, and the YOLOV3 framework uses the annotated images to automatically identify and count RBCs, WBCs, and platelets.

Keywords: YOLO, Machine Learning, YOLOv3, labelImg, RBC, WBC, Blood Cells

PDF | DOI: 10.17148/IJARCCE.2022.11734

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