A position-enhanced sequential feature encoding model for lung infections and lymphoma classification on CT images.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jul 14, 2024

Abstract

PURPOSE: Differentiating pulmonary lymphoma from lung infections using CT images is challenging. Existing deep neural network-based lung CT classification models rely on 2D slices, lacking comprehensive information and requiring manual selection. 3D models that involve chunking compromise image information and struggle with parameter reduction, limiting performance. These limitations must be addressed to improve accuracy and practicality.

Authors

  • Rui Zhao
  • Wenhao Li
    Flight Control Research Institute, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China.
  • Xilai Chen
    Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Yuchong Li
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Baochun He
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
  • Yucong Zhang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yu Deng
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
  • Chunyan Wang
    School of Food Science, Henan Institute of Science and Technology, Xinxiang, 453003 China.
  • Fucang Jia
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen, 518055, China.

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