Combining DC-GAN with ResNet for blood cell image classification.
In medicine, white blood cells (WBCs) play an important role in the human immune system. The different types of WBC abnormalities are related to different diseases so that the total number and classification of WBCs are critical for clinical diagnosis and therapy. However, the traditional method of...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 6; pp. 1251 - 1265 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143136821&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143136821 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2020 vid: 58 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143136821 143136821 NLM32221797 10.1007/s11517-020-02163-3 NLM32221797 143136821 ppf: 1251 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Combining DC-GAN with ResNet for blood cell image classification. aug: au: Ma, Li Shuai, Renjun Ran, Xuming Liu, Wenjia Ye, Chao affil: College of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China sug: subj: Leukocytes Image Processing, Computer Assisted Methods Blood Cells Scales ab: In medicine, white blood cells (WBCs) play an important role in the human immune system. The different types of WBC abnormalities are related to different diseases so that the total number and classification of WBCs are critical for clinical diagnosis and therapy. However, the traditional method of white blood cell classification is to segment the cells, extract features, and then classify them. Such method depends on the good segmentation, and the accuracy is not high. Moreover, the insufficient data or unbalanced samples can cause the low classification accuracy of model by using deep learning in medical diagnosis. To solve these problems, this paper proposes a new blood cell image classification framework which is based on a deep convolutional generative adversarial network (DC-GAN) and a residual neural network (ResNet). In particular, we introduce a new loss function which is improved the discriminative power of the deeply learned features. The experiments show that our model has a good performance on the classification of WBC images, and the accuracy reaches 91.7%. Graphical Abstract Overview of the proposed method, we use the deep convolution generative adversarial networks (DC-GAN) to generate new samples that are used as supplementary input to a ResNet, the transfer learning method is used to initialize the parameters of the network, the output of the DC-GAN and the parameters are applied the final classification network. In particular, we introduced a modified loss function for classification to increase inter-class variations and decrease intra-class differences. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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