COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images
Open accessAbstract
COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions.
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The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices.
“The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices [137–140].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [138]
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- Authors
- Muhammad, G., & Shamim Hossain, M.
- Affiliations
- King Saud University (SA)
- ORCID
- Muhammad, Ghulam
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 72
- Pages
- 80-88
- ISSN
- 1566-2535 (ISSN-L)
- PMC
- PMC7904462
- OpenAlex
- W3130844452
- Cited by
- 120
- References
- 37
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 19 · 2023: 25 · 2022: 36 · 2021: 22
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Information Fusion (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- King Saud University Deanship of Scientific Research
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 detection, lung ultrasound, multi-layer fusion, convolutional neural networks, COVID-19 screening, ultrasound image classification
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