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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 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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Hu, H., Dey, D., Del Giorno, A., Hebert, M., & Bagnell, J. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.00002 · Cited by 25
Quantitative analysis of ultrasound images for computer-aided diagnosis
Wu, J. Y., Tuomi, A., Beland, M. D., Konrad, J., Glidden, D. V., Grand, D., & Merck, D. L. (2016) · Journal of medical imaging · doi:10.1117/1.jmi.3.1.014501 · Cited by 21
Latest Methods of Image Enhancement and Restoration for Computed Tomography: A Concise Review
Al‐Ameen, Z., Al-Ameen, S., & Sulong, G. (2014) · Applied Medical Informatics (University of Medicine and Pharmacy Cluj-Napoca) · Cited by 17
Computed Tomography of the Lung
Verschakelen, J., & Wever, W. D. (2017) · Medical radiology · doi:10.1007/978-3-642-39518-5 · Cited by 17
3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnosticsPreprint
Khvostikov, A. V., Aderghal, K., Krylov, A. S., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03972 · Cited by 13
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
Chen, Z., Zhang, J., Ding, R., & Marculescu, D. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093418 · Cited by 11
Wang, Y., Wang, H., Guo, Y., Ning, C.-P., Liu, B., Cheng, H., & Tian, J. (2009) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-009-9245-1 · Cited by 8
CT Imaging: Practical Physics, Artifacts, and Pitfalls
Mamourian, A. C. (2013) · Cited by 6
Machine Learning Techniques for Prostate Ultrasound Image Diagnosis
Hassanien, A. E., Al-Qaheri, H., Snåšel, V., & Peters, J. F. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05177-7_19 · Cited by 4
Flagged first, then by citations
5 of the 110 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- 2020, RSNA Challenge
- 2020, OPENCV Histograms-2 Histogram Equalization
- 2020, Nih Dataset
- 2020, Who COVID-19 Situation Reports
- bell, 2020, COVID-19 Radiopaedia
- 2020, ACR Recommendations
Cited by
Sorted by citations
A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope
Salehi, A. W., Khan, S., Gupta, G., Alabduallah, B. I., Almjally, A., Alsolai, H., Siddiqui, T., & Mellit, A. (2023) · Sustainability · doi:10.3390/su15075930 · Cited by 682
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2022.3206407 · Cited by 345
Jiang, H., Diao, Z., Shi, T., Zhou, Y., Wang, F., Hu, W., Zhu, X., Luo, S., Tong, G., & Yao, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106726 · Cited by 244
Transfer learning for medical images analyses: A survey
Yu, X., Wang, J., Hong, Q., Teku, R., Wang, S., & Zhang, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.08.159 · Cited by 241
Metadata
- 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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