TFCNet: A texture-aware and fine-grained feature compensated polyp detection network.

Journal: Computers in biology and medicine
Published Date: Feb 14, 2024

Abstract

PURPOSE: Abnormal tissue detection is a prerequisite for medical image analysis and computer-aided diagnosis and treatment. The use of neural networks (CNN) to achieve accurate detection of intestinal polyps is beneficial to the early diagnosis and treatment of colorectal cancer. Currently, image detection models using multi-scale feature processing perform well in polyp detection. However, these methods do not fully consider the misalignment of information in the process of feature scale change, resulting in the loss of fine-grained features, and eventually cause the missed and false detection of targets.

Authors

  • Xiaoying Pan
    School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, PR China.
  • Yaya Mu
    Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China.
  • Chenyang Ma
    Shanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, 710121, China; School of Computer Science & Technology, Xi'an University of Post & Telecommunications, Xi'an, 710121, China.
  • Qiqi He
    School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China.

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