Abstract
White blood cell (WBC) segmentation and classification contribute significantly to developing computer-aided detection system. Several approaches have been proposed by different researchers. In this manuscript, we investigate in an approach for segmenting and classifying WBCs using image processing technique and deep learning. The WBC segmentation is a combination of the thresholding method and Watershed algorithm. The types of leukocytes (WBCs) are classified by a deep learning model. The experiments were evaluated on a publicly available dataset with large number of images. The segmentation result is promising. The proposed approach attained the average classification accuracy among five WBC types of 99.4%, the minimum accuracy was 98.6% corresponding to monocyte while the highest accuracy was 99.8% corresponding to eosinophil. The proposed approach also attained the classification accuracy of 99.6%, 99.2%, and 99.4% respectively for basophil, lymphocyte, and neutrophil.
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Acknowledgment
This research is partly funded by Industrial University of Ho Chi Minh city under grant number 55/HĐ-ĐHCN.
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Huynh, H.T., Dat, V.V.T., Anh, H.B. (2021). White Blood Cell Segmentation and Classification Using Deep Learning Coupled with Image Processing Technique. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_27
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DOI: https://doi.org/10.1007/978-981-16-8062-5_27
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