The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
Open accessAbstract
Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals’ precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (−ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.
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Cites
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MethodNarin, A., Kaya, C., & Pamuk, Z. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-00984-y · Cited by 2,054
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Background
Cited by
Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070
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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
Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
Ahuja, S., Panigrahi, B. K., Dey, N., Rajinikanth, V., & Gandhi, T. K. (2021) · Applied Intelligence · doi:10.1007/s10489-020-01826-w · Cited by 380
Transfer learning techniques for medical image analysis: A review
Kora, P., Ooi, C. P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W. Y., Meenakshi, K., Swaraja, K., Plawiak, P., & Rajendra Acharya, U. (2022) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.11.004 · Cited by 367
Metadata
- Authors
- Singh, D., Kumar, V., Vaishali, & Kaur, M.
- Venue
- European Journal of Clinical Microbiology & Infectious Diseases
- Published
- 2020-04-27
- Volume
- 39
- Issue
- 7
- Pages
- 1379-1389
- ISSN
- 0934-9723 (ISSN-L)
- 1435-4373
- OpenAlex
- W3017403618
- Cited by
- 647
- References
- 10
- FWCI
- 61.63
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Singh, D., Kumar, V., Vaishali, & Kaur, M. (2020). Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases. https://doi.org/10.1007/s10096-020-03901-z
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