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A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network.

Abstract：

:A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network is proposed. Firstly, a Siamese network with two identical convolutional neural network (CNN) sub-networks and a logistic regression for leukocyte five classification is designed, which can learn not only distinguishing features but also a similarity metric. Then for each category of the leukocytes, a typical sample is selected by the hematologist. To train the Siamese network, a leukocyte and a typical sample that belong to the same category make up a genuine pair and the leukocyte with the rest four typical samples respectively make up four impostor pairs. Obviously, the number of the genuine pairs is lesser than that of the impostor pairs. Thus, a data augmentation method suitable for leukocyte is used to enrich the amount of the genuine pairs. By training the Siamese network using the genuine pairs and impostor pairs, the Siamese network can not only shorten the similarity metric between the leukocyte and the same category of the typical sample but also increase the similarity metrics between the leukocyte and the different categories of the typical samples. Experimental results indicate that the proposed method can achieve 98.8% average testing accuracy. Graphical abstract.

journal_name

Med Biol Eng Comput

journal_title

Medical & biological engineering & computing

authors

Wang Y,Cao Y

doi

10.1007/s11517-020-02180-2

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

1575-1582

issue

7

eissn

0140-0118

issn

1741-0444

pii

10.1007/s11517-020-02180-2

journal_volume

58

pub_type

杂志文章

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