ArticleBiomedical signal processing and control2023
SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images.
Article in Biomedical signal processing and control, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Materials Advances in Devices for Heart Disease Interventions.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification.PeerJ. Computer science · 2024Article
- Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images.PloS one · 2024Article
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Authors and funding
5 authors.
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Abstract
The automatic segmentation of lung lesions from COVID-19 computed tomography (CT) images is helpful in establishing a quantitative model to diagnose and treat COVID-19. To this end, this study proposes a lightweight segmentation network called the SuperMini-Seg. We propose a new module called the transformer parallel convolution module (TPCB), which introduces both transformer and convolution operations in one module. SuperMini-seg adopts the structure of a double-branch parallel to downsample the image and designs a gated attention mechanism in the middle of the two parallel branches. At the same time, the attentive hierarchical spatial pyramid (AHSP) module and criss-cross attention module are adopted, and more than 100K parameters are present in the model. At the same time, the model is scalable, and the parameter quantity of SuperMini-seg-V2 reaches more than 70K. Compared with other advanced methods, the segmentation accuracy was almost reached the state-of-art method. The calculation efficiency was high, which is convenient for practical deployment.
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