Image detection method for multi-category lesions in wireless capsule endoscopy based on deep learning models.

Journal: World journal of gastroenterology
PMID: 39735271

Abstract

BACKGROUND: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.

Authors

  • Zhi-Guo Xiao
    School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
  • Xian-Qing Chen
    School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
  • Dong Zhang
    Institute of Acoustics, Nanjing University, Nanjing 210093, China.
  • Xin-Yuan Li
    School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
  • Wen-Xin Dai
    School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
  • Wen-Hui Liang
    School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.

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