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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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Abstract
Leukocyte detection and segmentation (WBCs) are a crucial stage in haematology imaging; a significant part in the diagnosis and management of serious illnesses is played by cell categorisation, particularly that of Leukocytes. White blood cells are also referred to as leukocytes which act as a foundation of the immune system. There are different approaches for the detection of WBC, but there are fewer papers which classify the type of WBC. The different approaches include Deep learning techniques and Support Vector Machine (SVM). The proposed methodology used deep learning strategies using the Convolutional Neural Network (CNN). Utilising blood cell imaging and the White blood cells (WBC), sequential image cropping technique and created a dataset of WBC. Then using CNN, the classification of images and type of WBC is determined as output. Through our CNN model, we can overcome the data augmentation by contemplating the blood cell images dataset and generate an accuracy of 99.191% by producing relevant results.
Keyword
Convolutional Neural Network, Leukocytes, sequential image cropping technique, Deep Learning,
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