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Detection of COVID-19 Using Machine Learning

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

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Abstract

In this paper, we aimed at experimenting and developing suitable machine learning algorithms along with some deep learning architectures, to achieve the task of COVID-19 classification. We experimented with various supervised learning algorithms for the classification of COVID-19 images and normal images, from X-ray images and radiography images. We evaluated the performance of our models on performance metrics like accuracy, precision, recall, F1 score, and Cohen Kappa score. The proposed model achieved the accuracy of 98.5%, F1 score of 97%, precision of 95%, and Cohen Kappa score of 0.88. In this experiment, we jotted down the values of performance of various performance metrics, that were obtained after tuning various parameters of the model and on different image resolutions of input image. We performed linear regression on the results to study the behavior of various performance metrics when we tuned our model with different parameters with different input image resolutions.

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References

  1. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., Xia, L.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases. Radiology 296(2), E32–E40 (2020)

    Article  Google Scholar 

  2. Alpaydin, E.: Introduction to Machine Learning. MIT Press (2020)

    Google Scholar 

  3. Anastasopoulos, C., Weikert, T., Yang, S., Abdulkadir, A., Schmülling, L., Bühler, C., Paciolla, F., Sexauer, R., Cyriac, J., Nesic, I., et al.: Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: the synergetic effect of an open, clinically embedded software development platform and machine learning. Eur. J. Radiol. 131, 109233 (2020)

    Article  Google Scholar 

  4. Apostolopoulos, I.D., Mpesiana, T.A.: COVID-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med. 43(2), 635–640 (2020)

    Article  Google Scholar 

  5. Bonaccorso, G.: Machine Learning Algorithms. Packt Publishing Ltd (2017)

    Google Scholar 

  6. Brooks, W.A.: Bacterial pneumonia. In: Hunter’s Tropical Medicine and Emerging Infectious Diseases, pp. 446–453. Elsevier (2020)

    Google Scholar 

  7. Brunese, L., Martinelli, F., Mercaldo, F., Santone, A.: Machine learning for coronavirus COVID-19 detection from chest X-rays. Proc. Comput. Sci. 176, 2212–2221 (2020)

    Article  Google Scholar 

  8. Burdick, H., Lam, C., Mataraso, S., Siefkas, A., Braden, G., Dellinger, R.P., McCoy, A., Vincent, J.L., Green-Saxena, A., Barnes, G., et al.: Prediction of respiratory decompensation in COVID-19 patients using machine learning: the ready trial. Comput. Biol. Med. 124, 103949 (2020)

    Article  Google Scholar 

  9. Chang, Y.C., Chang, K.H., Wu, G.J.: Application of extreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Appl. Soft Comput. 73, 914–920 (2018)

    Article  Google Scholar 

  10. Chowdhury, M.E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B., Islam, K.R., Khan, M.S., Iqbal, A., Al Emadi, N., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665–132676 (2020)

    Article  Google Scholar 

  11. Dai, S., Li, L., Li, Z.: Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access 7, 38287–38296 (2019)

    Article  Google Scholar 

  12. Deng, X., Shao, H., Shi, L., Wang, X., Xie, T.: A classification-detection approach of COVID-19 based on chest X-ray and CT by using Keras pre-trained deep learning models. Comput. Model. Eng. Sci. 125(2), 579–596 (2020)

    Google Scholar 

  13. Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., Ji, W.: Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2), E115–E117 (2020)

    Article  Google Scholar 

  14. Hamed, A., Sobhy, A., Nassar, H.: Accurate classification of COVID-19 based on incomplete heterogeneous data using a KNN variant algorithm. Arab. J. Sci. Eng. 1–12 (2021)

    Google Scholar 

  15. Holmes, K.V.: Sars-associated coronavirus. New Engl. J. Med. 348(20), 1948–1951 (2003)

    Article  Google Scholar 

  16. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497–506 (2020)

    Article  Google Scholar 

  17. Jeni, L.A., Cohn, J.F., De La Torre, F.: Facing imbalanced data-recommendations for the use of performance metrics. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 245–251. IEEE (2013)

    Google Scholar 

  18. Jin, X., Lian, J.S., Hu, J.H., Gao, J., Zheng, L., Zhang, Y.M., Hao, S.R., Jia, H.Y., Cai, H., Zhang, X.L., et al.: Epidemiological, clinical and virological characteristics of 74 cases of coronavirus-infected disease 2019 (COVID-19) with gastrointestinal symptoms. Gut 69(6), 1002–1009 (2020)

    Article  Google Scholar 

  19. Khanday, A.M.U.D., Rabani, S.T., Khan, Q.R., Rouf, N., Din, M.M.U.: Machine learning based approaches for detecting COVID-19 using clinical text data. Int. J. Inf. Technol. 12(3), 731–739 (2020)

    Google Scholar 

  20. Kwekha-Rashid, A.S., Abduljabbar, H.N., Alhayani, B.: Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl. Nanosci. 1–13 (2021)

    Google Scholar 

  21. Luke, J.J., Joseph, R., Balaji, M.: Impact of image size on accuracy and generalization of convolutional neural networks (2019)

    Google Scholar 

  22. Ozsahin, I., Sekeroglu, B., Mok, G.S.: The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer’s disease using Alzheimer’s disease neuroimaging initiative database. PLoS One 14(12), e0226577 (2019)

    Article  Google Scholar 

  23. Pyrc, K., Jebbink, M., Vermeulen-Oost, W., Berkhout, R., Wolthers, K., Wertheim-van, P.D., Kaandorp, J., Spaargaren, J., Berkhout, B., et al.: Identification of a new human coronavirus. Nat. Med. 10(4), 368–373 (2004)

    Article  Google Scholar 

  24. Yılmaz, N., Sekeroglu, B.: Student performance classification using artificial intelligence techniques. In: International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions, pp. 596–603. Springer (2019)

    Google Scholar 

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Correspondence to Saurav Kumar .

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Kumar, S., Tripathi, R. (2023). Detection of COVID-19 Using Machine Learning. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_13

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