Abstract
COVID-19 is causing a pandemic situation around the globe and its rapid spread is very alarming for us. It is also affecting our economy. Now it is time to automatic identification of COVID-19 because of avoiding the time-consuming testing processes and erroneous conditions to detect COVID-19. In this research, we have proposed an ensemble machine learning-based technique for detecting COVID-19. We also compared the result with other existing deep learning-based approaches. Here a publicly available SARS-CoV2 computerized tomography (CT) scan dataset is used which contains 2482 CT-scan images including 1252 COVID positive cases and 1230 negative cases. Linear discriminant analysis is used to reduce the dimensionality of our dataset. We have applied state-of-the-art machine learning techniques and compared their accuracy. Random forest and extreme gradient boosting provide 99% plus accuracy which is almost the same and better than other works that use deep learning techniques. The proposed method will be supportive for people and policymakers to detect COVID-19 automatically and reduce the suffering of the general people.
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Deb Mohalder, R., Sarder, A., Asif Hossain, K., Paul, L., Tazmim Pinki, F. (2023). Ensemble Machine Learning Technique for Identifying COVID-19 from CT Scan Images. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_2
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