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Microscopic blood smear segmentation and classification using deep contour aware CNN and extreme machine learning

conference contribution
posted on 2017-01-01, 00:00 authored by Imran RazzakImran Razzak, S Naz
© 2017 IEEE. Recent advancement in genomics technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. In this work, we have presented efficient contour aware segmentation approach based based on fully conventional network whereas for classification we have used extreme machine learning based on CNN features extracted from each segmented cell. We have evaluated system performance based on segmentation and classification on publicly available dataset. Experiment was conducted on 64000 blood cells and dataset is divided into 80% for training and 20% for testing. Segmentation results are compared with the manual segmentation and found that proposed approach provided with 98.12% and 98.16% for RBC and WBC respectively whereas classification accuracy is shown on publicly available dataset 94.71% and 98.68% for RBC & its abnormalities detection and WBC respectively.

History

Event

Computer Vision and Pattern Recognition. Conference Workshops (2017 : Honolulu, Hawaii)

Volume

2017-July

Pagination

801 - 807

Publisher

IEEE

Location

Honolulu, Hawaii

Place of publication

Piscataway, N.J.

Start date

2017-07-21

End date

2017-07-26

ISSN

2160-7508

eISSN

2160-7516

ISBN-13

9781538607336

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

CVPRW 2017 : IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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