Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction.

Journal: Computer methods and programs in biomedicine
PMID: 31786452

Abstract

BACKGROUND AND OBJECTIVE: Videocapsule endoscopy (VCE) is a relatively new technique for evaluating the presence of villous atrophy in celiac disease patients. The diagnostic analysis of video frames is currently time-consuming and tedious. Recently, computer-aided diagnosis (CAD) systems have become an attractive research area for diagnosing celiac disease. However, the images captured from VCE are susceptible to alterations in light illumination, rotation direction, and intestinal secretions. Moreover, textural features of the mucosal villi obtained by VCE are difficult to characterize and extract. This work aims to find a novel deep learning feature learning module to assist in the diagnosis of celiac disease.

Authors

  • Xinle Wang
    School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, China.
  • Haiyang Qian
    School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, China.
  • Edward J Ciaccio
    Department of Medicine, Celiac Disease Center, Columbia University, New York, USA.
  • Suzanne K Lewis
    Columbia University Medical Center, Department of Medicine - Celiac Disease Center, New York, USA.
  • Govind Bhagat
    Columbia University Medical Center, Department of Medicine - Celiac Disease Center, New York, USA; Columbia University Medical Center, Department of Pathology and Cell Biology, New York, USA.
  • Peter H Green
    Department of Medicine, Celiac Disease Center, Columbia University, New York, USA.
  • Shenghao Xu
    Key Laboratory of Sensor Analysis of Tumor Marker, Ministry of Education, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, PR China.
  • Liang Huang
    School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, China.
  • Rongke Gao
    School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei 230009, China. Electronic address: rkgao@hfut.edu.cn.
  • Yu Liu
    Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China.

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