ArticleApplied soft computing2022
COVID-WideNet-A capsule network for COVID-19 detection.
Article in Applied soft computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Review
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Analysis of healthcare data security with DWT-HD-SVD based-algorithm invisible watermarking against multi-size watermarks.Scientific reports · 2024Article
- COVision: convolutional neural network for the differentiation of COVID-19 from common pulmonary conditions using CT scans.BMC pulmonary medicine · 2023Article
- Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study.Scientific reports · 2023Article
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 2023Article
- Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.Journal of clinical medicine · 2023Review
- A COVID-19 medical image classification algorithm based on Transformer.Scientific reports · 2023Article
- A hybrid deep learning approach for COVID-19 detection based on genomic image processing techniques.Scientific reports · 2023Article
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale.BMC medical informatics and decision making · 2022Article
- TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19.Biocybernetics and biomedical engineeringArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been proved to provide encouraging results with higher accuracy as compared to the conventional method of RT-PCR testing. Chest radiography, particularly using X-ray images, is a prime imaging modality for detecting the suspected COVID-19 patients. However, the performance of these approaches still needs to be improved. In this paper, we propose a capsule network called COVID-WideNet for diagnosing COVID-19 cases using Chest X-ray (CXR) images. Experimental results have demonstrated that a discriminative trained, multi-layer capsule network achieves state-of-the-art performance on the
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