The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms
Open accessAbstract
COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era. The COVID-19 has proved much more pervasive demands for diagnosis that has driven researchers to develop more intelligent, highly responsive and efficient detection methods. In this work, we focus on proposing AI tools that can be used by radiologists or healthcare professionals to diagnose COVID-19 cases in a quick and accurate manner. However, the lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task. To this end, this study aims to build a comprehensive dataset of X-rays and CT scan images from multiple sources as well as provides a simple but an effective COVID-19 detection technique using deep learning and transfer learning algorithms. In this vein, a simple convolution neural network (CNN) and modified pre-trained AlexNet model are applied on the prepared X-rays and CT scan images dataset. The result of the experiments shows that the utilized models can provide accuracy up to 98 % via pre-trained network and 94.1 % accuracy by using the modified CNN.
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Cites
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Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
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“In additional to its efficiency in diagnosing the infection, it can estimate the level of severity of pneumonia [9].”
BackgroundDeep Learning for Medical Image Processing: Overview, Challenges and the Future
Razzak, M. I., Naz, S., & Zaib, A. (2018) · Lecture Notes in Computational Vision and Biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,248
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Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
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Cited by
Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913
Self-citationConvolutional neural networks in medical image understanding: a survey
Sarvamangala, D. R., & Kulkarni, R. V. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-020-00540-3 · Cited by 917
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data
Horry, M. J., Chakraborty, S., Paul, M., Ulhaq, A., Pradhan, B., Saha, M., & Shukla, N. (2020) · IEEE Access · doi:10.1109/access.2020.3016780 · Cited by 472
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays
Rajaraman, S., Siegelman, J., Alderson, P. O., Folio, L. S., Folio, L. R., & Antani, S. K. (2020) · IEEE Access · doi:10.1109/access.2020.3003810 · Cited by 365
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
Metadata
- Authors
- Maghdid, H., Asaad, A. T., Ghafoor, K. Z. G., Sadiq, A. S., Mirjalili, S., & Khan, M. K. K.
- Venue
- Multimodal Image Exploitation and Learning 2021
- Publisher
- SPIE
- Published
- 2021-04-12
- Pages
- 26
- OpenAlex
- W3014537881
- Cited by
- 459
- References
- 13
- FWCI
- 131.40
- Type
- conference paper
- Language
- en
- Open access
- green
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- Maghdid, H., Asaad, A. T., Ghafoor, K. Z. G., Sadiq, A. S., Mirjalili, S., & Khan, M. K. K. (2021). Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms. Multimodal Image Exploitation and Learning 2021. https://doi.org/10.1117/12.2588672
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