Lightweight Lung-nodule Detection Model Combined with Multidimensional Attention Convolution.

Journal: Current medical imaging
PMID: 39757654

Abstract

BACKGROUND: Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications.

Authors

  • He-He Huang
    Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
  • Yuetao Zhao
    School of Life Sciences, Central South University, Changsha 410013, China.
  • Sen-Yu Wei
    Department of Respiratory Medicine, the Affiliated Hospital of Jiangsu University, Zhenjiang 212001, China.
  • Chen Zhao
    Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
  • Yu Shi
    NIH BD2K Program Centers of Excellence for Big Data Computing-KnowEng Center, Department of Computer Science, University of Illinois at Urbana-Champaign , Champaign, Illinois.
  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Weijia Huang
    Department of Gastrointestinal Gland Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China; Guangxi Key Laboratory of Enhanced Recovery after Surgery for Gastrointestinal Cancer, Nanning, China; Guangxi Clinical Research Center for Enhanced Recovery after Surgery, Nanning, China; Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, Nanning, China.
  • Yifei Yang
    Safety Evaluation Center for Chinese Materia Medica, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
  • Jianhua Xu
    School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.

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