FecalNet: Automated detection of visible components in human feces using deep learning.

Journal: Medical physics
Published Date: Jul 18, 2020

Abstract

PURPOSE: To automate the detection and identification of visible components in feces for early diagnosis of gastrointestinal diseases, we propose FecalNet, a method using multiple deep neural networks.

Authors

  • Qiaoliang Li
    School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Shiyu Li
    Department of Biomedical Engineering, ShenZhen University, ShenZhen, 518000, China.
  • Xinyu Liu
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Zhuoying He
    Department of Biomedical Engineering, ShenZhen University, ShenZhen, 518000, China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ying Xu
    School of Biological and Food Engineering Changzhou University Changzhou Jiangsu China.
  • Huimin Guan
    Department of Biomedical Engineering, ShenZhen University, ShenZhen, 518000, China.
  • Runmin Chen
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, School of Medicine, Shenzhen University, Xueyuan Avenue, Nanshan District, Shenzhen, 518071, China.
  • Suwen Qi
    Department of In vitro Diagnostics, School of Biomedical Engineering, Shenzhen University, Shenzhen 518037, China.
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.

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