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DVT: Application of Deep Visual Transformer in Cervical Cell Image Classification

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Information Technology in Biomedicine (ITIB 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1429))

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Abstract

Cervical cancer is a very common cancer among women. Cytopathologists use a microscope to examine cell slides collected from the patient’s cervix to determine if it is cancerous. However, manually checking the slides to diagnose cancer is a very difficult task, not only time-consuming but also error-prone. A computer-aided diagnosis system can automatically and accurately screen cervical cell images. In this study, we propose a framework called DVT to perform classification tasks. We use SIPaKMeD and CRIC datasets together. On 11-class classification tasks, DVT achieves an accuracy of 87.35%. In the comparative experiment, DVT performs better than the CNN and VT models alone.

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Author information

Authors and Affiliations

  1. Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Wanli Liu, Chen Li, Weiming Hu & Haoyuan Chen

  2. Shengjing Hospital, China Medical University, Shenyang, China

    Hongzan Sun

  3. Institute of Medical Informatics, University of Luebeck, Luebeck, Germany

    Marcin Grzegorzek

Authors
  1. Wanli Liu
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  2. Chen Li
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  3. Hongzan Sun
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  4. Weiming Hu
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  5. Haoyuan Chen
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  6. Marcin Grzegorzek
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Corresponding author

Correspondence to Chen Li .

Editor information

Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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Liu, W., Li, C., Sun, H., Hu, W., Chen, H., Grzegorzek, M. (2022). DVT: Application of Deep Visual Transformer in Cervical Cell Image Classification. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_24

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