IMAGING SCIENCE AND PHOTOCHEMISTRY ›› 2022, Vol. 40 ›› Issue (5): 1024-1028.DOI: 10.7517/issn.1674-0475.220522

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Study of Colorectal Cancer CT Image Classification Based on 3D Convolutional Network

LYU Gang1, YING Mingliang2   

  1. 1. Graphic Information Center, Jinhua Radio and Television University, Jinhua 321000, Zhejiang, P. R. China;
    2. Department of Radiology, Jinhua Municipal Central Hospital, Jinhua 321000, Zhejiang, P. R. China
  • Received:2022-05-27 Published:2022-09-13

Abstract: Colorectal cancer (CRC) is among the top three tumors in the world. At present, screening CRC in CT images still needs to be done manually by radiologists, which is a time-consuming and laborious repetitive work. This paper investigated a method which extracted 3D features on abdominal CT images based on 3D convolutional network, then estimated whether there were CRC lesions through CT images. This paper collected a colorectal cancer CT image dataset with 348 samples and designed three 3D convolution networks with different structures, and performed a normal/abnormal binary classification experiments on the acquired abdominal CT images. The best performance model in this paper was a 3D convolution networks with three improved residual modules. The average accuracy of normal/abnormal binary classification experiment was 96.2%, and the AUC was 0.989, which was 2.2% and 2.9% higher than the baseline model. The experiment results showed that the 3D convolutional networks with have outstanding performance in the CRC normal/abnormal classification task, it also has good generalization ability and only under CT-level weak-labeled data, it is helpful to clinical application.

Key words: 3D convolution, residual learning, CT image classification, deep learning