ArticleScientific reports2023
Densely attention mechanism based network for COVID-19 detection in chest X-rays.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
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- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Segmentation of gastroesophageal reflux events using a semi-U-Net architecture with 1D/2D CNNs.Scientific reports · 2025Article
- Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: a focus on feature reproducibility.Discover oncology · 2025Article
- Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system.Scientific reports · 2025Article
- Brain CT image classification based on mask RCNN and attention mechanism.Scientific reports · 2024Article
- Improving privacy-preserving multi-faceted long short-term memory for accurate evaluation of encrypted time-series MRI images in heart disease.Scientific reports · 2024Article
- A high-order focus interaction model and oral ulcer dataset for oral ulcer segmentation.Scientific reports · 2024Article
- Attention-enhanced dilated convolution for Parkinson's disease detection using transcranial sonography.Biomedical engineering online · 2024Article
- Parkinson's image detection and classification based on deep learning.BMC medical imaging · 2024Article
- Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data.Diagnostics (Basel, Switzerland) · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI.BMC medical imaging · 2023Article
- Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images.Scientific reports · 2023Article
- COVID-Net Biochem: an explainability-driven framework to building machine learning models for predicting survival and kidney injury of COVID-19 patients from clinical and biochemistry data.Scientific reports · 2023Article
- COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.Journal of digital imaging · 2023Article
- Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing.BMC medical informatics and decision making · 2023Article
- DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.Bioengineering (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.
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