  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. Manual
screening of cytopathology images is time-consuming and error-prone. 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.
