Automatic Detection of Covid-19 from Chest X-Rays using Weighted Average Ensemble Framework

Authors

  • P. V. Naresh Research Scholar, Annamalai University, Department of Computer Science and Engineering, Tamil Nadu, India
  • R. Visalakshi Assistant Professor. Annamalai University, Department of Information Technology, Tamil Nadu, India
  • B. Satyanarayana Principal, CMR Institute of Technology, Telangana, India

Keywords:

Covid -19, VGG16, ResNet50, InceptionV3, weighted average ensemble model

Abstract

COVID-19 stands as one of the most serious diseases resulting from the novel corona virus. Chest x-rays (CXR) have gained widespread recognition as an effective screening tool for lung-related diseases due to their simplicity and cost-effectiveness. Radiologists find CXR image interpretation straightforward and affordable due to its low cost and fast results. In this study, we introduce an innovative approach to improve the detecting and classification of Covid-19 by analyzing their chest x-rays(CXR). Our model includes pretrained architectures like VGG16, ResNet50, and InceptionV3 to create a Weighted Average Ensemble Model. Despite the limitation of dataset our model has achieved an accuracy rate of 98.33%. These results confirm the potential of weighted average ensemble models in contribution to identifying Covid-19 through chest x-ray-based classification.

Downloads

Download data is not yet available.

References

C.D.C. COVID, R. Team, Severe outcomes among patients with coronavirus disease 2019 (COVID-19)—United States, February 12–March 16, 2020, MMWR Morb. Mortal Wkly. Rep. 69 (2020) 343–346.

A. Remuzzi, G. Remuzzi, COVID-19 and Italy: what next? Lancet (2020). E. Mahase, Coronavirus: covid-19 has killed more people than SARS and MERS combined,

World Health Organization. “Public health emergency of international concern (PHEIC),” who. 2020. p. 1–10 [Online]. Available: https://www.who.int/publications/m/item/COVID-19-public-health-emergency-of-international-concern-(ph eic)-global-research-and-innovation-forum. [Accessed 2 August 2021]. accessed.

C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet 395 (2020) 497–506.

V.M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D.K.W. Chu, T. Bleicker, S. Brünink, J. Schneider, M.L. Schmidt, Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR, Eurosurveillance 25 (2020), 2000045.

O. Gozes, M. Frid-Adar, H. Greenspan, P.D. Browning, H. Zhang, W. Ji, A. Bernheim, E. Siegel, Rapid ai development cycle for the coronavirus (covid-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis, ArXivPrepr. ArXiv2003.05037. (2020).

Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G. Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology 2019;291 (1):196–202. http://dx.doi.org/10.1148/radiol.2018180921

Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging 2019;49(4):939–54.

http://dx.doi.org/10.1002/jmri.26534

I. Castiglioni, D. Ippolito, M. Interlenghi, C.B. Monti, C.Salvatore, S. Schiaffino, A. Polidori, D. Gandola, C. Messa, F. Sardanelli, Artificial intelligence applied on chest x-ray can aid in the diagnosis of covid-19 infection: a first experience from lombardy, italy, medRxivdoi: 10.1101/ 2020.04.08.20040907.

Ozturk T , Talo M , Yildirim EA , Baloglu UB , Yildirim O , Acharya UR . Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 2020:103792.

Nigam B, Nigam A, Jain R, Dodia S, Arora N, Annappa B. Covid-19: Automatic detection from x-ray images by utilizing

deep learning methods. Expert Syst Appl 2021;176 . https:// doi.org/10.1016/j.eswa.2021.114883 114883.

Kumar P. and Kumari S. Detection of coronavirus disease (COVID-19) based on deep features. preprints.org, no. March, p. 9, Mar. 2020.

R. Jain, M. Gupta, S. Taneja, D.J. Hemanth, Deep learning based detection and analysis of COVID-19 on chest X-ray images, Appl. Intell. 51 (3) (2021) 1690–1700.

Narayanan BN, Hardie RC, Krishnaraja V, Karam C, Davuluru VSP. Transfer-to-transfer learning approach for computer aided detection of covid-19 in chest radiographs. AI 2020;1(4):539–57.

AnandbabuGopatoti a, P. Vijayalakshmi “ CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer” Biomedical Signal Processing and Control 77 (2022) 10380

Kunwar Prashant , Prakash Choudhary,b, Tarun Agrawal , Evam Kaushik , “OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique”, Intelligent Systems with Applications 16 (2022) 200154, https://doi.org/10.1016/j.iswa.2022.200154

A. Rahman, H. Zunair, M. Sohel Rahman, J. Quader Yuki, S. Biswas, M. Ashraful Alam, N. Binte Alam, M.R.C. Mahdy, Improving Malaria Parasite Detection from Red Blood Cell Using Deep Convolutional Neural Networks, 2019 arXiv Preprint arXiv:1907.10418.

Pradeep Kumar Roy, Abhinav Kumar, “Early Prediction of Covid-19 using ensemble of transfer learning”, Computers and Electrical Engineering 101 (2022)10818, https://doi.org/10.1016/j.compeleceng.2022.108018

G. Ayana, K. Dese, S. Choe, Transfer learning in breast cancer diagnoses via ultrasound imaging, Cancers 13 (2021) 738.

Transfer learning. Available online: http://www.isikdogan.com/blog/transfer-learning.html (accessed on 15 March 2022).

Simonyan, K. & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

Ekbal, A., & Saha, S. (2013). Stacked ensemble coupled with feature selection for biomedical entity extraction. Knowledge-Based Systems, 46, 22–32.

P.V. Naresh, R. Visalakshi, B.Satyanarayana,” PSO Optimized CNN-SVM Architecture for Covid - 19 Classification”, International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 11 Issue: 5s, https://doi.org/10.17762/ijritcc.v11i5s.6646

P.V. Naresh, R. Visalakshi, B.Satyanarayana,”A Light Weight Grid Search Based Ensemble Model for Covid-19 Classification in Chest X-Rays” Intelligent Systems and applications in engineering,”Volume 11 issue : 3.

Downloads

Published

20.10.2023

How to Cite

Naresh, P. V. ., Visalakshi, R. ., & Satyanarayana, B. . (2023). Automatic Detection of Covid-19 from Chest X-Rays using Weighted Average Ensemble Framework. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 608–614. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3681

Issue

Vol. 12 No. 2s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.