Journal of Guangdong University of Technology ›› 2025, Vol. 42 ›› Issue (1): 24-32.doi: 10.12052/gdutxb.230177

• Smart Medical • Previous Articles    

Lung Tumor Segmentation Method Based on Transformer and Attention Mechanisms

Zeng An1, Wang Dan1, Yang Baoyao1, Zhang Xiaobo2, Shi Zhenwei3, Liu Zaiyi3, Pan Dan4   

  1. 1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China;
    2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    3. Guangdong Provincial People's Hospital, Guangzhou 510080, China;
    4. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China
  • Received:2023-11-11 Published:2025-01-06

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Abstract: The accurate segmentation of lung tumors plays a crucial role in tumor diagnosis and treatment. However, lung tumor segmentation is often challenged by several issues such as low contrast between lesions and surrounding tissues, tumor-normal tissue adhesion, and high background noise. To address these, this study introduces a lung tumor segmentation method based on Transformer and attention mechanisms. In the Transformer encoder stage, both global and local attention mechanisms are incorporated to enable the network to simultaneously focus on both global and local contextual information. In the skip connection stage, a channel-prior convolutional attention mechanism is utilized to enhance the spatial perception ability for complex lesions and reduce the channel dimension redundancy, such that the tumor segmentation accuracy can be improved. The experimental results on the private GDPH and public LUNG1 datasets demonstrate that the proposed method outperforms eight comparative methods in terms of the Dice metric by achieving approximately 90.96% and 88.18% on the two datasets, respectively. The proposed method can provide reliable assistance for clinical diagnosis and treatment.

Key words: lung tumor, medical image segmentation, convolutional neural networks, Transformer, attention mechanisms

CLC Number: 

  • TP391.4
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