Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (2): 347-360.doi: 10.1007/s11390-021-0849-3

Special Issue: Emerging Areas

• Special Section on AI and Big Data Analytics in Biology and Medicine • Previous Articles     Next Articles

CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology

Hua Chen1, Juan Liu1,*, Senior Member, CCF, Qing-Man Wen1, Zhi-Qun Zuo1, Jia-Sheng Liu1, Jing Feng1, Bao-Chuan Pang2, and Di Xiao2        

  1. 1 Institute of Artificial Intelligence, School of Computer Science, Wuhan University, Wuhan 430072, China;
    2 Landing Artificial Intelligence Center for Pathological Diagnosis, Wuhan University, Wuhan 430072, China
  • Received:2020-07-30 Revised:2021-02-17 Online:2021-03-05 Published:2021-04-01
  • Contact: Juan Liu
  • About author:Hua Chen is a Ph.D. candidate in the School of Computer Science, Wuhan University, Wuhan. His current research interests include deep learning, medical image processing, and image classification and segmentation.
  • Supported by:
    This work was supported by the Major Projects of Technological Innovation in Hubei Province of China under Grant Nos. 2019AEA170 and 2019ACA161, the Frontier Projects of Wuhan for Application Foundation under Grant No. 2019010701011381, and the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University under Grant No. ZNJC201919.

Identification of abnormal cervical cells is a significant problem in computer-aided diagnosis of cervical cancer. In this study, we develop an artificial intelligence (AI) system, named CytoBrain, to automatically screen abnormal cervical cells to help facilitate the subsequent clinical diagnosis of the subjects. The system consists of three main modules: 1) the cervical cell segmentation module which is responsible for efficiently extracting cell images in a whole slide image (WSI); 2) the cell classification module based on a compact visual geometry group (VGG) network called CompactVGG which is the key part of the system and is used for building the cell classifier; 3) the visualized human-aided diagnosis module which can automatically diagnose a WSI based on the classification results of cells in it, and provide two visual display modes for users to review and modify. For model construction and validation, we have developed a dataset containing 198 952 cervical cell images (60 238 positive, 25 001 negative, and 113 713 junk) from samples of 2 312 adult women. Since CompactVGG is the key part of CytoBrain, we conduct comparison experiments to evaluate its time and classification performance on our developed dataset and two public datasets separately. The comparison results with VGG11, the most efficient one in the family of VGG networks, show that CompactVGG takes less time for either model training or sample testing. Compared with three sophisticated deep learning models, CompactVGG consistently achieves the best classification performance. The results illustrate that the system based on CompactVGG is efficient and effective and can support for large-scale cervical cancer screening.

Key words: cervical cancer screening; visual geometry group (VGG); deep learning; artificial intelligence (AI); classification;

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