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dc.contributor.authorLin, Sheng-Fuuen_US
dc.contributor.authorTseng, Chien-Haoen_US
dc.contributor.authorHuang, Chung-Ien_US
dc.date.accessioned2014-12-08T15:36:12Z-
dc.date.available2014-12-08T15:36:12Z-
dc.date.issued2014en_US
dc.identifier.isbn978-3-03785-947-6en_US
dc.identifier.issn1660-9336en_US
dc.identifier.urihttp://hdl.handle.net/11536/24550-
dc.identifier.urihttp://dx.doi.org/10.4028/www.scientific.net/AMM.479-480.491en_US
dc.description.abstractIn this paper, the application of the supervised learning system to automatic classification of leukocytes processing for the microscopic images analysis is presented. The traditional pattern classification in cellular images is typically made by experienced operators. Such procedures may present a non-standard and unstable accuracy when it depends on the operator\'s capabilities and tiredness. In this study, we propose the supervised learning system to achieve an automated segmentation and classification of leukocytes based on supervised neural networks and image processing methods. The experimental results show that the proposed automatic classification learning system can effectively classify the five types of the leukocytes in microscopic cell images, as well as to compare the classification results to those obtained by the medical experts.en_US
dc.language.isoen_USen_US
dc.subjectAutomatic cell classificationen_US
dc.subjectmultilayer perceptron (MLP)en_US
dc.subjectmorphological analysisen_US
dc.subjectimage processingen_US
dc.titleSupervised Neural Networks for the Automatic Classification of Leukocytes in Blood Microscope Imagesen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.4028/www.scientific.net/AMM.479-480.491en_US
dc.identifier.journalAPPLIED SCIENCE AND PRECISION ENGINEERING INNOVATION, PTS 1 AND 2en_US
dc.citation.volume479-480en_US
dc.citation.spage491en_US
dc.citation.epage495en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000337850800094-
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