P Rajarajeswari
A deep learning computational approach for the classification of Covid-19 virus
Rajarajeswari, P; Santhi, K; Chattopadhyay, P; Beg, OA
Abstract
Deep learning and transfer learning are being extensively adopted in biomedical, health and wellbeing related applications. The continuous Covid 2019 (COVID-19) pandemic brought about an
extreme impact on the worldwide medical services framework, mainly as a result of its simple
transmission and the all-encompassing time of the infection endurance on polluted surfaces. As
per a worldwide agreement proclamation from the Fleischner Society, registered computer
tomography (CT) is an applicable screening instrument owing to its higher efficiency in identifying
early pneumonic changes since lung infection is a major manifestation of the covid 19 virus.
Notwithstanding, doctors are still very involved battling COVID-19 in this period of overall
emergency and new variants of the virus are emerging (delta, omicron) even after two years since
the start of the pandemic. Hence, it is urgent to speed up the advancement of a man-made
consciousness (AI) indicative device to help doctors. Regardless of colossal endeavors, it remains
extremely difficult to create a powerful model to aid the exact measurement appraisal of COVID19 from the chest CT pictures. Due to the idea of obscured limits, regulated division techniques
generally experience the ill effects of explanation predispositions. This image-based finding, it is
envisaged will achieve significant improvements in more rapidly, effectively and accurately
identifying Covid contamination in human beings. In this paper we have proposed CNN
(convolutional neural network) based multi-picture growth procedure for recognizing COVID-19
in CT scans of Covid speculated patients. Multi-picture expansion utilizes irregularity data
obtained from the shifted pictures for preparing the CNN model. We have proposed framework
implements deep learning via multi-faceted CNN and with this methodology, the proposed
Citation
Rajarajeswari, P., Santhi, K., Chattopadhyay, P., & Beg, O. (2022). A deep learning computational approach for the classification of Covid-19 virus. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, https://doi.org/10.1080/21681163.2022.2111722
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 18, 2022 |
Online Publication Date | Aug 19, 2022 |
Publication Date | Aug 19, 2022 |
Deposit Date | Aug 22, 2022 |
Publicly Available Date | Aug 20, 2023 |
Journal | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
Print ISSN | 2168-1163 |
Publisher | Taylor and Francis |
DOI | https://doi.org/10.1080/21681163.2022.2111722 |
Publisher URL | https://doi.org/10.1080/21681163.2022.2111722 |
Additional Information | Additional Information : This is an Accepted Manuscript of an article published by Taylor & Francis in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization on August 19th 2022, available at: http://www.tandfonline.com/10.1080/21681163.2022.2111722 |
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Deep learning algorithm for visualization of covid 19 scanning
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