The DOI matches its Crossref record; 37 of the 39 references it cites were found in a registry or an index.
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients
Open accessAbstract
Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray (CXR) images in automatic detection of COVID-19 cases. We present a synergistic approach to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations and leverage a Siamese network for final classification of COVID-19 cases. We validate the effectiveness of our proposed model using two publicly available datasets comprising images from normal, COVID-19 and other pneumonia infected categories. Our model achieves 95.6% accuracy and AUC of 0.97 in diagnosing COVID-19 from CXR images even with a limited number of training samples.
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“In 2020, DL is playing an increasingly vital role in early diagnosis of the novel coronavirus (COVID-19) [29, 46–48].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [48]
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Metadata
- Authors
- Shorfuzzaman, M., & Hossain, M. S.
- Affiliations
- Taif University (SA)
- King Saud University (SA)
- Corresponding author
- Hossain, M. Shamim
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 113
- Pages
- 107700
- ISSN
- 0031-3203 (ISSN-L)
- PMC
- PMC7568501
- OpenAlex
- W3093282011
- Cited by
- 217
- References
- 39
- Citations by year
- 2026: 9 · 2025: 25 · 2024: 28 · 2023: 39 · 2022: 65 · 2021: 48
- FWCI
- 18.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Pattern Recognition (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deanship of Scientific Research, King Saud University
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- COVID-19 diagnosis, chest X-ray images, Siamese network, contrastive learning, meta-learning, limited training data, pneumonia classification, medical image classification
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