TY - JOUR
T1 - CVM-Cervix
T2 - A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron
AU - Liu, Wanli
AU - Li, Chen
AU - Xu, Ning
AU - Jiang, Tao
AU - Rahaman, Md Mamunur
AU - Sun, Hongzan
AU - Wu, Xiangchen
AU - Hu, Weiming
AU - Chen, Haoyuan
AU - Sun, Changhao
AU - Yao, Yudong
AU - Grzegorzek, Marcin
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - Cervical cell classification
KW - Convolutional neural network
KW - Image classification
KW - Multilayer perceptron
KW - Pap smear
KW - Visual transformer
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U2 - 10.1016/j.patcog.2022.108829
DO - 10.1016/j.patcog.2022.108829
M3 - Article
AN - SCOPUS:85131458963
SN - 0031-3203
VL - 130
JO - Pattern Recognition
JF - Pattern Recognition
M1 - 108829
ER -