ArticleComputers in biology and medicine2021
D2A U-Net: Automatic segmentation of COVID-19 CT slices based on dual attention and hybrid dilated convolution.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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26 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Automated segmentation of COVID-19 lesions in CT scans using attention U-net with hybrid loss functions.Scientific reports · 2026Article
- Performance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management Architecture.Sensors (Basel, Switzerland) · 2025Article
- CI-UNet: melding convnext and cross-dimensional attention for robust medical image segmentation.Biomedical engineering letters · 2024Article
- Article
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- Semisupervised Deep Learning for the Detection of Foreign Materials on Poultry Meat with Near-Infrared Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2023Article
- A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.Cardiovascular engineering and technology · 2023Article
- CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images.Diagnostics (Basel, Switzerland) · 2023Article
- PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation.Evolving systems · 2023Article
- Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.Biomedical signal processing and control · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.Biomedical signal processing and control · 2023Article
- COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.International journal of imaging systems and technology · 2023Article
- Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.Medical & biological engineering & computing · 2022Article
- HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.Computers in biology and medicine · 2022Article
- DMDF-Net: Dual multiscale dilated fusion network for accurate segmentation of lesions related to COVID-19 in lung radiographic scans.Expert systems with applications · 2022Article
- A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases.Biomedical signal processing and control · 2022Article
- One Shot Model For The Prediction of COVID-19 And Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features.Applied soft computing · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Disease 2019 (COVID-19) has become one of the most urgent public health events worldwide due to its high infectivity and mortality. Computed tomography (CT) is a significant screening tool for COVID-19 infection, and automatic segmentation of lung infection in COVID-19 CT images can assist diagnosis and health care of patients. However, accurate and automatic segmentation of COVID-19 lung infections is faced with a few challenges, including blurred edges of infection and relatively low sensitivity. To address the issues above, a novel dilated dual attention U-Net based on the dual attention strategy and hybrid dilated convolutions, namely D2A U-Net, is proposed for COVID-19 lesion segmentation in CT slices. In our D2A U-Net, the dual attention strategy composed of two attention modules is utilized to refine feature maps and reduce the semantic gap between different levels of feature maps. Moreover, the hybrid dilated convolutions are introduced to the model decoder to achieve larger receptive fields, which refines the decoding process. The proposed method is evaluated on an open-source dataset and achieves a Dice score of 0.7298 and recall score of 0.7071, which outperforms the popular cutting-edge methods in the semantic segmentation. The proposed network is expected to be a potential AI-based approach used for the diagnosis and prognosis of COVID-19 patients.
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