Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms

Authors

  • Abdelhai LATI Faculty of New information and communication technologies University Kasdi Merbah Ouargla (UKMO), BP 511, 30000, Ouargla, Algeria
  • Khaled BENSID Lab. de Génie Electrique (LAGE)
  • Ibtissem LATI Faculty of Medicine,Ouargla (UKMO), BP 511, 30000, Ouargla. Algeria.
  • Chahra GEZZAL Lab. de Génie Electrique (LAGE),Faculty of New information and communication technologies University Kasdi Merbah Ouargla (UKMO), BP 511, 30000, Ouargla. Algeria

Abstract

The detection of the novel coronavirus disease (COVID-19) has recently become a critical task for medical diagnosis. Knowing that deep Learning is an advanced area of machine learning that has gained much of interest, especially convolutional neural network. It has been widely used in a variety of applications. Since it has been proved that transfer learning is effective for the medical classification tasks,
in this study; COVID -19 detection system is implemented as a quick alternative, accurate and reliable diagnosis option to detect COVID-19 disease. Three pre-trained convolutional neural network based models (ResNet50, VGG19, AlexNet) have been proposed for this system. Based on the obtained performance results, the pre-trained models with support vector machine (SVM) provide the best classification performance compared to the used models individually.

Keywords

COVID-19, Support Vector Machine (SVM), VGG19, AlexNet, ResNet50

References

Stoecklin, S. B., Rolland, P., Silue, Y., Mailles, A., Campese, C., Simondon, A., and Levy-Bruhl, D. (2020). First cases of coronavirus disease 2019 (COVID-19) in France: surveillance, investigations and control measures, January 2020. Eurosurveillance, 25(6), 2000094.

Akter, S., Shamrat, F. J. M., Chakraborty, S., Karim, A., & Azam, S. (2021). COVID-19 detection using deep learning algorithm on chest X-ray images. Biology, 10(11), 1174.

A. Narin, C. Kaya, and Z. Pamuk, ―Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks,‖ Pattern Analysis and Applications, vol. 24, no. 3, pp. 1207–1220, 2021.

L. Wang, Z. Q. Lin, and A. Wong, ―Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images,‖ Scientific Reports, vol. 10, no. 1, pp. 1–12, 2020.

E. E.-D. Hemdan, M. A. Shouman, and M. E. Karar, ―Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images,‖ arXiv preprint arXiv:2003.11055, 2020..

J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia, ―Covid-19 screening on chest x-ray images using deep learning based anomaly detection,‖ arXiv preprint arXiv:2003.12338, vol. 27, 2020.

Abbas, A.; Abdelsamea, M.M.; Medhat Gaber, M. Classification of COVID-19 in Chest X-ray Images Using DeTraC Deep Convolutional Neural Network. Appl. Intell. 2021, 51, 854–864.

Hall, L.; Goldgof, D.; Paul, R.; Goldgof, G.M. Finding COVID-19 from Chest X-rays Using Deep Learning on a Small Dataset. arXiv 2020 arXiv:2004.02060.

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

Q. Ji, J. Huang, W. He, and Y. Sun, ―Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images,‖ Algorithms, vol. 12, no. 3, p. 51, 2019.

Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolution neural networks, C. Proceedings of Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2012: 1106–1114.

Abdullah, D. M., and Abdulazeez, A. M. (2021). Machine learning applications based on Svm classification a review. Qubahan Academic Journal, 1(2), 81-90.

Tang, Y. (2013). Deep learning using support vector machines. CoRR, abs/1306.0239, 2, 1.

Jakkula, V. (2006). Tutorial on support vector machine (svm). School of EECS, Washington State University, 37(2.5), 3.

The used data set : https://www.kaggle.com/datasets. Access on 19-12-2021

Bäck, J. (2019). Domain similarity metrics for predicting transfer learning performance. Linköping University | Department of Computer and Information Science Master Thesis, 30 ECTS | Data vetenskap 2018 | liu-ida/lith-ex-a--18/046—se.

Gillard, L., Bellot, P., & El-Bèze, M. (2006, May). Question Answering Evaluation Survey. In LREC (pp. 1133-1138).

Published

2023-10-17

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