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Cervical cancer is the seventh most common cancer among all the cancersworldwide and the fourth most common cancer among women. Cervical cytopathologyimage classification is an important method to diagnose cervical cancer. Manualscreening of cytopathology images is time-consuming and error-prone. Theemergence of the automatic computer-aided diagnosis system solves this problem.This paper proposes a framework called CVM-Cervix based on deep learning toperform cervical cell classification tasks. It can analyze pap slides quicklyand accurately. CVM-Cervix first proposes a Convolutional Neural Network moduleand a Visual Transformer module for local and global feature extractionrespectively, then a Multilayer Perceptron module is designed to fuse the localand global features for the final classification. Experimental results show theeffectiveness and potential of the proposed CVM-Cervix in the field of cervicalPap smear image classification. In addition, according to the practical needsof clinical work, we perform a lightweight post-processing to compress themodel.

CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron