Determination of Covid-19 Possible Cases by Using Deep Learning Techniques

Abstract

A large number of cases have been identified in the world with the emergence of COVID-19 and the rapid spread of the virus. Thousands of people have died due to COVID-19. This very spreading virus may result in serious consequnces including pneumonia, kidney failure acute respiratory infection. It can even cause death in severe cases. Therefore, early diagnosis is vital. Due to the limited number of COVID-19 test kits, one of the first diagnostic techniques in suspected COVID-19 patients is to have Thorax Computed Tomography (CT) applied to individuals with suspected COVID-19 cases when it is not possible to administer these test kits. In this study, it was aimed to analyze the CT images automatically and to direct probable COVID-19 cases to PCR test quickly in order to make quick controls and ease the burden of healthcare workers. ResNet-50 and Alexnet deep learning techniques were used in the extraction of deep features. Their performance was measured using Support Vector Machines (SVM), Nearest neighbor algorithm (KNN), Linear Discrimination Analysis (LDA), Decision trees, Random forest (RF) and Naive Bayes methods as the methods of classification. The best results were obtained with ResNet-50 and SVM classification methods. The success rate was found as 95.18%.

Keywords

• Resnet-50
• Alexnet
• Deep Learning
• COVID-19
• Classification

References

1. [1]R. Sujath, J. M. Chatterjee and A. E. Hassanien, “A machine learning forecasting model for COVID-19 pandemic in India,” Stochastic Environmental Research and Risk Assessment, 1, Springer, 2020.
2. [2] E. Mahase, “Coronavirus: global stocks of protective gear are depleted, with demand at 100 times normal level, WHO warns,” British Medical Journal Publishing Group,2020.
3. [3] C. Columbus, K. B. Brust and A. C. Arroliga. "2019 novel coronavirus: an emerging global threat," Baylor University Medical Center Proceedings, vol. 33, no. 2, Taylor & Francis, 2020.
4. [4] Y. Mohamadou, A. Halidou and P. T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19,” Applied Intelligence, Springer, pp. 1-13, 2020.
5. [5] P. K. Shukla, P. K. Shukla, P. Sharma, P. Rawat, J. Samar, R. Moriwal and M. Kaur, “Efficient prediction of drug–drug interaction using deep learning models,” IET Systems Biology, 2020.
6. [6] M. Kaur, H. K. Gianey, D. Singh and M. Sabharwal, “Multi-objective differential evolution based random forest for e-health applications,” Modern Physics LettersB, World Scientific, 33, 05, 2019.
7. [7] M. Kaur and D. Singh, “Fusion of medical images using deep belief Networks,” Cluster Computing, 1-15, 2019
8. [8] Y.Gu, X. Lu, L. Yang, B. Zhang, D. Yu, Y. Zhao and T. Zhou, “Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs,” Computers in biology and medicine, 103, pp. 220-231, 2018.

9. [9] S. S. Yadav and M. J. Shivajirao, "Deep convolutional neural network based medical image classification for disease diagnosis." Journal of Big Data 6.1, 113, 2019.
10. [10] M. Talo, “Pneumonia Detection from Radiography Images using Convolutional Neural Networks2019 27th Signal Processing and Communications Applications Conference (SIU), IEEE, pp. 1-4, 2019.
11. [11] V. Chouhan, S. K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira and V. H. C. De Albuquerque, “A novel transfer learning based approach for pneumonia detection in chest X-ray images,” Applied Sciences, 10(2), 559, 2020.
12. [12] R. H. Abiyev and M. K. S. Ma’aitah, “Deep convolutional neural networks for chest diseases detection,” Journal of healthcare engineering, Hindawi, 2018.
13. [13] T. Araújo, G. Aresta, E. Castro, J. Rouco, P. Aguiar, C. Eloy, A. Polonia and A. Campilho, “Classification of breast cancer histology images using convolutional neural networks,” PloS one, Public Library of Science San Franscisco, CA USA, 12, 6 e0177544, 2017.
14. [14] Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, S. Singh and P. K. Shukla, “Deep Transfer Learning based Classification Model for COVID-19 Disease,” IRBM, Elsevier, 2020.
15. [15] M. Toğaçar, B. Ergen and Z. Cömert, “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches,” Computers in Biology and Medicine, 103805, 2020.
16. [16] A. A. Ardakani, A. R. Kanafi, U. R. Acharya, N. Khadem and A. Mohammadi, “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural Networks,” Computers in Biology and Medicine, Elsevier, 103795,2020.
17. [17] P. K. Sethy and S. K. Behera “Detection of coronavirus disease (covid-19) based on deep features,” Preprints, 2020030300, 2020.
18. [18] X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv and G. Lang, “Deep learning system to screen coronavirus disease 2019 pneumonia,” arXiv 2020, arXiv preprint arXiv:2002.09334, 2020.
19. [19] L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu and M. Pietikäinen, “Deep learning for generic object detection: A survey,” International journal of computer vision, Springer, 128, 2, pp. 261-318, 2020.
20. [20] A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Zeitschrift für Medizinische Physik,Elsevier, 29, 2, pp. 102-127, 2019.
21. [21] E. Alpaydin, Introduction to machine learning. MIT pres, 2020.
22. [22] P. Lubaib and K. A. Muneer, “The heart defect analysis based on PCG signals using pattern recognition techniques,” Procedia Technology, Elsevier, 24, pp. 1024-1031, 2016.
23. [23] R. Nisbet, J. Elder and G. Miner, Handbook of statistical analysis and data mining applications, Academic Press, 2009.
24. [24] S. Balakrishnama and A. Ganapathiraju, “Linear discriminant analysis-a brief tutorial,” Institute for Signal and information Processing, vol. 18, no. 1998, pp. 1-8, 1998.
25. [25] A. Krizhevsky, I. Sutskever and G.E. Hinton, “Imagenet classification with deep convolutional neural net works,” Adv. Neural Inf. Process. Syst., pp. 1097–1105, 2012.
26. [26] X. Du, Y. Cai, S. Wang and L. Zhang, “Overview of deep learning”, 31st Youth Acad. Annu. Conf. Chinese Assoc. Autom., pp. 159–164, 2016.
27. [27] H. Byun and S. W. Lee, “A survey on pattern recognition applications of support vector machines,” International Journal of Pattern Recognition and Artificial Intelligence, 17,3, pp. 459-486, 2003.
28. [28] K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016
29. [29] A. Çinar and M. Yıldırım, “Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture,” Medical Hypotheses, 109684.
30. [30] https://www.kaggle.com/luisblanche
31. [31] M. Yağanoğlu and C. Köse “Real-Time Detection of Important Sounds with a Wearable Vibration Based Device for Hearing-Impaired People,” Electronics, 7(4), 50, 2018.
32. [32] F. Bozkurt, C. Köse and A. Sarı, “An inverse approach for automatic segmentation of carotid and vertebral arteries in CTA,” Expert Systems with Applications, 93, pp. 358-375, 2018.
33. [33] L. Breiman, “Random forests,” Machine learning, 45(1), 5-32, 2001.
34. [34] L. Breiman and A. Cutler, Random Forests, 2004, Retrieved from: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#prox