Pneumonia Classification and Recognition Method Based on Multi-resolution Attention Dense Network
ZHOU Tao1,2, YE Xinyu1,2, LU Huiling3, CHANG Xiaoyu1,2, LIU Yuncan1,2
1. Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021; 2. Department of Computer Science and Engineering, North Minzu University, Yinchuan 750021; 3. Department of Science, Ningxia Medical University, Yinchuan 750004
Abstract：X-ray film of pneumonia suffers from inconspicuous imaging features, low contrast between lesions and surrounding tissues, and blurred edges. Therefore, a pneumonia classification and recognition method based on multi-resolution attention dense network is proposed. Shallow localization information and deep semantic information are deeply fused. A multi-resolution spatial attention gate is constructed to enhance semantic interaction between deep and shallow information at different resolutions, establishing interdependency for lesion information in deep and shallow information. In addition, the coordinate frequency attention is designed to adaptively enhance the representation of pneumonia features in a complementary manner of orientation and location. Experiments on five pneumonia X-ray datasets including ChestXRay2017 show that the proposed method achieves better performance in pneumonia classification and recognition task with robustness on the public pneumonia dataset.
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2023, Vol. 36 
