Multi-disease X-ray Image Classification of the Chest Based on Global and Local Fusion Adaptive Networks.

Journal: Current medical imaging
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms to automatically analyze and identify X-ray images, determining whether specific pathologies or structural abnormalities exist in the images.

Authors

  • Yu Gu
    Microsoft Research, Redmond, WA, USA.
  • Ru Shi
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Shuaikang Yang
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Lidong Yang
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Baohua Zhang
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Xiaoqi Lu
    School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China; Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China. Electronic address: lxiaoqi@imut.edu.cn.
  • Jianjun Li
    Rehabilitation Clinic, Shenzhen University General Hospital, Shenzhen, Guangdong, China.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Ying Zhao
    Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Dahua Yu
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
  • Siyuan Tang
    Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China.
  • Qun He
    Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, 014010, China.

Keywords

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