Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis. (June 2023)

• Record Type: Journal Article
• Authors: Xie, Tingyi; Wang, Zidong; Li, Han; Wu, Peishu; Huang, Huixiang; Zhang, Hongyi; Alsaadi, Fuad E.; Zeng, Nianyin
• Is Part Of: Computers in biology and medicine. Volume 159(2023)
• Publication Date: 2023-06
• Subjects: Imperfect data -- Artificial intelligence -- Attention mechanism -- Medical imaging analysis -- Progressive learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106947
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this paper, a novel deep learning-based medical imaging analysis framework is developed, which aims to deal with the insufficient feature learning caused by the imperfect property of imaging data. Named as multi-scale efficient network (MEN), the proposed method integrates different attention mechanisms to realize sufficient extraction of both detailed features and semantic information in a progressive learning manner. In particular, a fused-attention block is designed to extract fine-grained details from the input, where the squeeze-excitation (SE) attention mechanism is applied to make the model focus on potential lesion areas. A multi-scale low information loss (MSLIL)-attention block is proposed to compensate for potential global information loss and enhance the semantic correlations among features, where the efficient channel attention (ECA) mechanism is adopted. The proposed MEN is comprehensively evaluated on two COVID-19 diagnostic tasks, and the results show that as compared with some other advanced deep learning models, the proposed method is competitive in accurate COVID-19 recognition, which yields the best accuracy of 98.68% and 98.85%, respectively, and exhibits satisfactory generalization ability as well. Highlights: The combination of ECA and SE blocks promotes feature extraction capability. Progressive feature fusion is realized in the information refinement module. Global information loss is compensated in the MSLIL attention block.