Automatic detection and classification of leukocytes using convolutional neural networks.
The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slow...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 8; pp. 1287 - 1302 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Aug2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=124485687&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124485687 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2017 vid: 55 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 124485687 124485687 143978271 NLM27822698 10.1007/s11517-016-1590-x NLM27822698 124485687 ppf: 1287 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Automatic detection and classification of leukocytes using convolutional neural networks. aug: au: Zhao, Jianwei Zhang, Minshu Zhou, Zhenghua Chu, Jianjun Cao, Feilong affil: Department of Applied Mathematics, College of Science , China Jiliang University , Hangzhou 310018 People's Republic of China sug: subj: Image Interpretation, Computer Assisted Methods Information Science Methods Microscopy Methods Neural Networks (Computer) Leukemia, Lymphocytic, Acute Pathology Leukocytes Pathology Algorithms Sensitivity and Specificity Cells, Cultured Reproducibility of Results ab: The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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