期刊
PATTERN RECOGNITION
卷 130, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2022.108829
关键词
Convolutional neural network; Visual transformer; Multilayer perceptron; Cervical cell classification; Pap smear; Image classification
资金
- National Natural Science Foundation of China [61806047]
This paper proposes a deep learning-based framework called CVM-Cervix to perform cervical cell classification tasks, utilizing Convolutional Neural Network and Visual Transformer modules for local and global feature extraction, showing effectiveness and potential in the field of cervical Pap smear image classification.
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 CVMCervix 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.(c) 2022 Elsevier Ltd. All rights reserved.
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