Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)

<Previous Article In Volume
Next Article In Volume>

Deep Convolutional Neural Network based Solution for Detection of COVID-19 from Chest X-Ray Images

Authors
Y. Rama Mohan1, D. Satyanarayana1, *, R. Sudheer Babu2, K. Ashfaq Ahmed1, M. Siva Satyanarayana3
1Department of Computer Science and Engineering (AIML), G. Pulla Reddy Engineering College (Autonomous), Kurnool, 518007, India
2Department of Electronics and Communication Engineering, G. Pulla Reddy Engineering College (Autonomous), Kurnool, 518007, India
3Department of Electrical and Electronics Engineering, G. Pulla Reddy Engineering College (Autonomous), Kurnool, 518007, India
*Corresponding author. Email: sathyaphd223@gmail.com
Corresponding Author
D. Satyanarayana
Available Online 17 March 2025.
DOI
10.2991/978-94-6463-662-8_23How to use a DOI?
Keywords
Covid-19; Ensemblemethods; CNN; DenseNet; ResNet; Inceptionv3
Abstract

A global health and healthcare catastrophe has been brought on by the ongoing COVID-19 pandemic, in addition to its substantial socioeconomic impacts. One of the primary issues in this pandemic situation is the timely identification and monitoring of COVID patients to make timely decisions regarding therapy, monitoring, and management. Research is underway to create less time-consuming methods to complement or replace RT-PCR-based techniques. In the current study, a deep convolutional neural network (DCNN) approach is proposed for identifying COVID-19+ patients from images of chest x-ray. To evaluate the effectiveness of this treatment, we examined publicly available chest x-ray scans of COVID+ patients. The 538 images of positive detected patients and the 468 images of negative detected patients were divided into 771 images for training and 235 images for testing. The present work provided accuracy with 95.7% in classification and sensitivity around 98% in test setup.

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.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
Series
Advances in Engineering Research
Publication Date
17 March 2025
ISBN
978-94-6463-662-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-662-8_23How 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  - Y. Rama Mohan
AU  - D. Satyanarayana
AU  - R. Sudheer Babu
AU  - K. Ashfaq Ahmed
AU  - M. Siva Satyanarayana
PY  - 2025
DA  - 2025/03/17
TI  - Deep Convolutional Neural Network based Solution for Detection of COVID-19 from Chest X-Ray Images
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 283
EP  - 298
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_23
DO  - 10.2991/978-94-6463-662-8_23
ID  - Mohan2025
ER  -