Abstract
One of the most challenging issues that humans face in the last decade is in the health sector, and it is threatening his existence. The COVID-19 is one of those health threats as declared by the World Health Organization (WHO). This spread of COVID-19 forced WHO to declare this virus as a pandemic in 2019. In this paper, COVID-19 chest X-rays classification through the fusion of deep transfer learning and machine learning methods will be presented. The dataset “DLAI3 Hackathon Phase3 COVID-19 CXR Challenge” is used in this research for investigation. The dataset consists of three classes of X-rays images. The classes are COVID-19, Thorax Disease, and No Finding. The proposed model is made up of two main parts. The first part for feature extraction, which is accomplished using three deep transfer learning algorithms: AlexNet, VGG19, and InceptionV3. The second part is the classification using three machine learning methods: K-nearest neighbor, support vector machine, and decision trees. The results of the experiments show that the proposed model using VGG19 as a feature extractor and support vector machine. It reached the highest conceivable testing accuracy with 97.4%. Moreover, the proposed model achieves a superior testing accuracy than VGG19, InceptionV3, and other related works. The obtained results are supported by performance criteria such as precision, recall, and F1 score.
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Khalifa, N.E.M., Taha, M.H.N., Chakrabortty, R.K., Loey, M. (2022). COVID-19 Chest X-rays Classification Through the Fusion of Deep Transfer Learning and Machine Learning Methods. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_1
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