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- W4311184445 abstract "White blood cell (WBC) counting is vital for the diagnostic of many diseases. However, the manual counting method operated in hospitals and centers is time-consuming and inefficient. Many automatic processes in classifying WBC types were proposed using deep learning (DL) models. YOLOv4 is an emerging model for detecting objects with promising results in many applications including the medical field. In this paper, YOLOv4’s performance would be tested on classifying four types of WBC: monocyte, lymphocyte, neutrophil, and eosinophil. A mixed database of WBC microscopic images from an online dataset BCCD and a hospital dataset collected from the Vietnamese National Institute of Hematology and Blood Transfusion, comprising of 10,275 images in total after the augmentation, was used for training and testing the model. The results showed that the trained YOLOv4 was able to classify equivalently well all 4 WBC types with the average accuracy of 97.8%, ranking the second-best option when compared to other contemporary studies." @default.
- W4311184445 created "2022-12-24" @default.
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- W4311184445 date "2022-12-03" @default.
- W4311184445 modified "2023-09-27" @default.
- W4311184445 title "Yolov4 in White Blood Cell Classification" @default.
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- W4311184445 doi "https://doi.org/10.1007/978-981-19-6004-8_31" @default.
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