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Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Fuat Türk*

Computer Engineering, Cankiri Karatekin University, Cankiri, 18100, Turkey

* Corresponding Author: Fuat Türk. Email: email

Computer Systems Science and Engineering 2023, 45(2), 1357-1373.


Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the negative results caused by the virus and the treatment costs, the importance of fast and accurate diagnosis is clearly seen. In this context, deep learning methods appear as an extremely popular approach. In this study, a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease. In addition, in order to contribute to the literature, a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented. With this ensemble model design, quite remarkable results are obtained for the diagnosis of three and four-class Covid-19. The proposed model can classify normal, pneumonia, and Covid-19 with 92.6% accuracy and 82.6% for normal, pneumonia, Covid-19, and lung opacity.


Cite This Article

F. Türk, "Covid-19 diagnosis using a deep learning ensemble model with chest x-ray images," Computer Systems Science and Engineering, vol. 45, no.2, pp. 1357–1373, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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