ArticleComputers in biology and medicine2022
COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Viral pneumonia detection during the COVID-19 pandemic using deep learning and DCGAN-based data augmentation.Scientific reports · 2026Article
- CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction.PLoS computational biology · 2026Article
- FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.Medical & biological engineering & computing · 2024Article
- Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2.Expert systems with applications · 2023Article
- Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.Viruses · 2023Article
- COVID-19 recognition from chest X-ray images by combining deep learning with transfer learning.Digital healthArticle
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Authors and funding
5 authors.
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Abstract
Aiming at detecting COVID-19 effectively, a multiscale class residual attention (MCRA) network is proposed via chest X-ray (CXR) image classification. First, to overcome the data shortage and improve the robustness of our network, a pixel-level image mixing of local regions was introduced to achieve data augmentation and reduce noise. Secondly, multi-scale fusion strategy was adopted to extract global contextual information at different scales and enhance semantic representation. Last but not least, class residual attention was employed to generate spatial attention for each class, which can avoid inter-class interference and enhance related features to further improve the COVID-19 detection. Experimental results show that our network achieves superior diagnostic performance on COVIDx dataset, and its accuracy, PPV, sensitivity, specificity and F1-score are 97.71%, 96.76%, 96.56%, 98.96% and 96.64%, respectively; moreover, the heat maps can endow our deep model with somewhat interpretability.
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