FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-110324

Research Article |

An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans

Author(s): P. V. Naresh*, R. Visalakshi and B. Satyanarayana

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 3

Publisher : FOREX Publication

Published : 23 September 2023

e-ISSN : 2347-470X

Page(s) : 794-799

DOI: https://doi.org/10.37391/ijeer.110324

Abstract

Covid has resulted in millions of deaths worldwide, making it crucial to develop fast and safe diagnostic methods to control its spread. Chest X-Ray imaging can diagnose pulmonary diseases, including Covid. Most research studies have developed single convolution neural network models ignoring the advantage of combining different models. An ensemble model has higher predictive accuracy and reduces the generalization error of prediction. We employed an ensemble of Multi Deep Neural Networks models for Covid.19 classification in chest X-Ray scans using Multiclass classification (Covid, Pneumonia, and Normal). We improved the accuracy by identifying the best parameters using the sklean Grid search technique and implementing it with the Optimized Weight Average Ensemble Model, which allows multiple models to predict. Our ensemble model has achieved 95.26% accuracy in classifying the X-Ray images; it demonstrates potential in ensemble models for diagnosis using Radiography images.

Keywords: Covid-19, VGG-16, ResNet50, InceptionV3, Ensemble.




P. V. Naresh*, Research Scholar, Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, India; Email: naresh.groups@gmail.com

R. Visalakshi, Assistant Professor, Department of Information Technology, Annamalai University, Tamil Nadu, India; Email: visalakshiau@yahoo.in

B. Satyanarayana, Professor, Department of Computer Science & Engineering, CMR Institute of Technology, Telangana, India; Email: bsat777@gmail.com

    [1] Shervin Minaee Rahele Kafieh Ghazaleh Jamali Soufi, “Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning”, Medical Image Analysis, vol. 65, Art. no. 101794, July 21, 2020 (Cover date: October 2020).
    [2] Tanvir Mahmud Md Awsafur Rahman Shaikh Anowarul Fattah, “CovXNet: A multi- dilation convolution neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization”, Computers in Biology and Medicine, vol. 122, Art. no. 103869, June 20, 2020 (Cover date: July 2020).
    [3] T.O. TaloU, R. Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Comput. Biol. Med. vol. 121, Art. no. 103792 (April 28, 2020). Cover date: June 2020.
    [4] Mohammad Rahimzadeh Abolfazl Attar, “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2”, Informatics in Medicine Unlocked, vol. 19, Art. no. 100360, May 26 2020 (Cover date: 2020).
    [5] G. Meyerowitz-Katz, S. Bhatt, O. Ratmann, J.M. Brauner, S. Flaxman, S. Mishra, M. Sharma, S. Mindermann, V. Bradley, M. Vollmer, et al., Is the cure really worse than the disease? The health impacts of lockdowns during COVID-19, BMJ Glob. Health 6 (8) (2021) e006653.
    [6] Subrat Sarangi, Uddeshya khanna, and Rohit kumar : “Ensemble Deep Convolution Neural Network for Sars-Cov-V2 Detection”, International Journal of Electrical and Electronics Research, Volume 10, Issue: 3, Page No: 481-486 , 2022.
    [7] V. Sanjay, P. Swarnalatha: Deep learning Techniques for Early Detection of Alzheimer’s Disease – A Review”, International Journal of Electrical and Electronics Research, Volume:10, Issue: 4, Pg No: 889-905.
    [8] Kabid Hassan Shibly Samrat Kumar Dey Md Mahbubur Rahman, “COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images”, Informatics in Medicine Unlocked, vol. 20, Art. no. 100405, August 1, 2020 (Cover date: 2020).
    [9] Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Rajendra Acharya, U. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792.
    [10] Farooq, M., Hafeez, A., Anjum, M. A., & Javed, M. Y. (2020). COVID-19 detection through transfer learning using multilayer neural network. IEEE Access, 8, 149808-149815
    [11] Apostolopoulos, I. D., &Mpesiana, T. A. (2020). Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640.
    [12] Kumar, A., Mittal, S., Khanna, A., Garg, I., & Jain, A. (2020). COVID-19 diagnosis using chest X-ray images through deep learning and convolutional neural networks. Proceedings of the International Conference on Intelligent Sustainable Systems, 1132-1137.
    [13] Singh, D., Kumar, V., Vaishali, K., Kumar, V., & Kumar, V. (2021). COVID-19 detection using transfer learning and chest X-rays images. Journal of Ambient Intelligence and Humanized Computing, 12(6), 5221-5231.
    [14] Narin, A., Kaya, C., Pamuk, Z. (2021). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24, 1207–1220.
    [15] Soares, E., Angelov, P., Biaso, S., Froes, M., & Krawczyk, B. (2020). COVID-19 detection in chest X-rays with deep learning. arXiv preprint arXiv:2004.02060.
    [16] Apostolopoulos, I. D., Aznaouridis, S. I., & Tzani, M. A. (2020). Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. Journal of Medical and Biological Engineering, 40(4), 462-469.
    [17] Agrawal, T., & Choudhary, P. (2022). Segmentation and classification on chest radiography: A systematic survey. The Visual Computer, 1–39. https://doi.org/ 10.1007/s00371-021-02352-7.
    [18] Dr. V. Gokula Krishnan, Dr. M. V. Vijaya Saradhi, Dr. S. Sai Kumar, G. Dhanalakshmi, P. Pushpa and Dr. V. Vijayaraja (2023), Hybrid Optimization based Feature Selection with DenseNet Model for Heart Disease Prediction. IJEER 11(2), 253-261. DOI: 10.37391/IJEER.110203.
    [19] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255). IEEE.
    [20] Ekbal, A., & Saha, S. (2013). Stacked ensemble coupled with feature selection for biomedical entity extraction. Knowledge-Based Systems, 46, 22–32.
    [21] V Sanjay and P Swarnalatha (2022), Deep Learning Techniques for Early Detection of Alzheimer’s Disease: A Review. IJEER 10(4), 899-905. DOI: 10.37391/IJEER.100425.
    [22] A.M. Ismael, A. Şengür, Deep learning approaches for COVID-19 detection based on chest X-Ray images, Expert Syst. Appl. 164 (2021) 114054.
    [23] 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.
    [24] Bejoy Abraham Madhu S. Nair, “Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier”, Biocybernetics and Biomedical Engineering, vol. 40, no. 4, pp. 1436-1445, September 2 2020 (Cover date: October–December 2020).
    [25] S.H. Khan, A. Sohail, M.M. Zafar, A. Khan, Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network, Photodiagnosis Photodyn. Ther. 35 (2021) 102473.

P. V. Naresh, R. Visalakshi and B. Satyanarayana (2023), An Adaptive Grid Search Based Efficient Ensemble Model for Covid-19 Classification in Chest X-Ray Scans. IJEER 11(3), 794-799. DOI: 10.37391/ijeer.110324.