Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)

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Deep Transfer Learning Platforms for SARS-CoV-19 Diagnostics based on Human Lungs CT Scan Imaging

Authors
Krishna Kumar Joshi1, *, Kamlesh Gupta2, Jitendra Agrawal3
1SoIT, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India
2Rustam Ji Institute of Technology, Tekanpur, Gwalior, Madhya Pradesh, India
3SoIT, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India
*Corresponding author. Email: krishnakjoshi@gmail.com
Corresponding Author
Krishna Kumar Joshi
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-872-1_18How to use a DOI?
Keywords
Transfer Learning; Deep Learning; CT Scan Imaging; SARS-CoV- 19; Critical Success Index; Diagnostic Ratio; False Omission Rate
Abstract

COVID-19 is one of the most serious diseases caused by the SARS coronavirus. This is a fatal disease, and it becomes difficult to save the life of the infected person because it progresses very rapidly and starts in the lungs and causing damage to the entire body. With the help of advanced machine learning techniques, this disease can be detected early and with very little probability of error. In this paper, we have developed two deep transfer learning models, VGG 16 and MobileNetv2, to detect COVID-19. Both models were applied to datasets based on human lung CT scan images, and their performance was evaluated. To analyze and compare their performance, we have used several performance met- rics such as Prevalence, Null Error Rate, False DR, Negative PV, False OR, LR ratio (+), LR ratio (-), CSI, Accuracy, FM Index, BM, Diagnostic Ratio, MK and Critical Success Index, that have not been used in previous papers.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
Series
Advances in Economics, Business and Management Research
Publication Date
4 November 2025
ISBN
978-94-6463-872-1
ISSN
2352-5428
DOI
10.2991/978-94-6463-872-1_18How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Krishna Kumar Joshi
AU  - Kamlesh Gupta
AU  - Jitendra Agrawal
PY  - 2025
DA  - 2025/11/04
TI  - Deep Transfer Learning Platforms for SARS-CoV-19 Diagnostics based on Human Lungs CT Scan Imaging
BT  - Proceedings of the 2nd International Conference on Sustainable Business Practices and Innovative Models (ICSBPIM-2025)
PB  - Atlantis Press
SP  - 236
EP  - 253
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-872-1_18
DO  - 10.2991/978-94-6463-872-1_18
ID  - Joshi2025
ER  -