Abstract
The novel coronavirus (COVID-19) pandemic has spread rapidly worldwide infecting over 164.3 million people with approximately 3.4 million deaths. An automatic and fast diagnosis of COVID-19 in the earliest stage is crucial to further avoid its easy person-to-person transmission and deaths. Over the past decade, deep learning algorithms have shown incredible performance in image recognition systems including medical diagnostics. Therefore, this paper presents deep learning-based multi-model ensemble classifier technique that consists of five different customized Convolutional Neural Networks (CNNs) trained on publicly available chest X-ray datasets. The results show that the majority voting ensemble technique provides better performances for both multi-class (COVID-19, normal, pneumonia), and binary (COVID-19 vs Normal) classifications. Furthermore, as COVID-19 resembles pneumonia, the proposed model successfully distinguishes between COVID-19 and pneumonia cases and achieved an overall accuracy of 97.3% for multi-class classification and 100% on binary classification.
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Khan, W., Zaki, N. (2022). COVID-19 Detection from Chest X-ray Using Deep Learning Ensemble Classifier. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_33
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DOI: https://doi.org/10.1007/978-981-16-6460-1_33
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