Cervical cell multi-classification algorithm using global context information and attention mechanism. (February 2022)

• Record Type: Journal Article
• Authors: Li, Jun; Dou, Qiyan; Yang, Haima; Liu, Jin; Fu, Le; Zhang, Yu; Zheng, Lulu; Zhang, Dawei
• Is Part Of: Tissue & cell. Volume 74(2022)
• Publication Date: 2022-02
• Subjects: Cervical cancer -- Cell classification -- Convolutional neural network -- Attention mechanism -- Long short-term memory; Cytology -- Periodicals; 571.5
• DOI: 10.1016/j.tice.2021.101677
• Languages: English
• ISSNs: 0040-8166

Abstract

Highlights: A multi classification cervical cell algorithm. Combined with the improved ResNet-50. Compared with different CNNs. Abstract: Cervical cancer is the second biggest killer of female cancer, second only to breast cancer. The cure rate of precancerous lesions found early is relatively high. Therefore, cervical cell classification has very important clinical value in the early screening of cervical cancer. This paper proposes a convolutional neural network (L-PCNN) that integrates global context information and attention mechanism to classify cervical cells. The cell image is sent to the improved ResNet-50 backbone network to extract deep learning features. In order to better extract deep features, each convolution block introduces a convolution block attention mechanism to guide the network to focus on the cell area. Then, the end of the backbone network adds a pyramid pooling layer and a long short-term memory module (LSTM) to aggregate image features in different regions. The low-level features and high-level features are integrated, so that the whole network can learn more regional detail features, and solve the problem of network gradient disappearance. The experiment is conducted on the SIPaKMeD public data set. The experimental results show that the accuracy of the proposed l -PCNN in cervical cell accuracy is 98.89 %, the sensitivity is 99.9 %, the specificity is 99.8 % and the F-measure is 99.89 %, which is better than most cervical cell classification models, which proves the effectiveness of the model.