CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron

Wanli Liu, Chen Li, Ning Xu, Tao Jiang, Md Mamunur Rahaman, Hongzan Sun, Xiangchen Wu, Weiming Hu, Haoyuan Chen, Changhao Sun, Yudong Yao, Marcin Grzegorzek

Research output: Contribution to journalArticlepeer-review

123 Scopus citations

Abstract

Cervical cancer is the seventh most common cancer among all the cancers worldwide and the fourth most common cancer among women. Cervical cytopathology image classification is an important method to diagnose cervical cancer. However, manual inspection is very troublesome, and experts are prone to make mistakes. The emergence of the automatic computer-aided diagnosis system solves this problem. This paper proposes a framework called CVM-Cervix based on deep learning to perform cervical cell classification tasks. It can analyze pap slides quickly and accurately. CVM-Cervix first proposes a Convolutional Neural Network module and a Visual Transformer module for local and global feature extraction respectively, then a Multilayer Perceptron module is designed to fuse the local and global features for the final classification. Experimental results show the effectiveness and potential of the proposed CVM-Cervix in the field of cervical Pap smear image classification. In addition, according to the practical needs of clinical work, we perform a lightweight post-processing to compress the model.

Original languageEnglish
Article number108829
JournalPattern Recognition
Volume130
DOIs
StatePublished - Oct 2022

Keywords

  • Cervical cell classification
  • Convolutional neural network
  • Image classification
  • Multilayer perceptron
  • Pap smear
  • Visual transformer

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