COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Open accessAbstract
Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitableConvolutional Neural Network(CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.
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The combination of multimodal data can predict and screen for COVID-19 more accurately.
“The latest literature indicates that the combination of multimodal data can predict and screen for this virus more accurately [106, 145].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [145]
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Nahiduzzaman, M., Goni, M. O. F., Islam, M. R., Sayeed, A., Anower, M. S., Ahsan, M., Haider, J., & Kowalski, M. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.06.003 · Cited by 34 · see citing works
Sahoo, P., Saha, S., Sharma, S. K., Mondal, S., & Gowda, S. P. M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121724 · Cited by 33 · see citing works
COVID-19 Detection using Transfer Learning with Convolutional Neural Network
Dutta, P., Roy, T. L. D., & Anjum, N. (2021) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest51555.2021.9331029 · Cited by 33 · see citing works
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Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive ReviewPreprint
Bharati, S., Podder, P., Mondal, M. R. H., & Prasath, V. B. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09602 · Cited by 48 · see citing works
COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms
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Analysis of COVID-19 and Pneumonia Detection in Chest X-Ray Images using Deep Learning
Kanakaprabha, S., & Radha, D. (2021) · International Conference on Communication, Control and Information Sciences (ICCISc) · doi:10.1109/iccisc52257.2021.9484888 · Cited by 32 · see citing works
Federated and transfer learning for cancer detection based on image analysis
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Applications and challenges of AI-based algorithms in the COVID-19 pandemic
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CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging
Saif, A. F. M., Imtiaz, T., Rifat, S., Shahnaz, C., Zhu, W., & Ahmad, M. O. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3104791 · Cited by 31 · see citing works
- Authors
- Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N.
- Affiliations
- University of Technology Sydney (AU)
- Charles Sturt University (AU)
- Corresponding author
- Chakraborty, Subrata
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 149808-149824
- ISSN
- 2169-3536 (ISSN-L)
- PMC
- PMC8668160
- OpenAlex
- W3049131298
- Cited by
- 472
- References
- 110
- Citations by year
- 2026: 16 · 2025: 33 · 2024: 54 · 2023: 86 · 2022: 149 · 2021: 124 · 2020: 10
- FWCI
- 44.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Victoria University Research Repository (Victoria University) (submittedVersion, cc-by)
- RUNE (Research UNE) (submittedVersion, cc-by)
- Charles Sturt University Research Output (CRO) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Griffith Research Online (submittedVersion, cc-by)
- 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 detection, transfer learning, multimodal medical imaging, chest radiography, ultrasonography, computed tomography, VGG19, convolutional neural networks, image preprocessing, image denoising, limited training data, pneumonia classification, automated disease diagnosis
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