Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms

Seyed Masoud Rezaeijo1, Mohammadreza Ghorvei2, Razzagh Abedi-Firouzjah3, Hesam Mojtahedi4, Hossein Entezari Zarch4
1Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
2Department of Electrical and Computer Engineering, Tarbiat Modares University, Al-Ahmad and Chamran Cross, Tehran, Iran
3Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
4School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Tóm tắt

Abstract Background This study aimed to propose an automatic prediction of COVID-19 disease using chest CT images based on deep transfer learning models and machine learning (ML) algorithms. Results The dataset consisted of 5480 samples in two classes, including 2740 CT chest images of patients with confirmed COVID-19 and 2740 images of suspected cases was assessed. The DenseNet201 model has obtained the highest training with an accuracy of 100%. In combining pre-trained models with ML algorithms, the DenseNet201 model and KNN algorithm have received the best performance with an accuracy of 100%. Created map by t-SNE in the DenseNet201 model showed not any points clustered with the wrong class. Conclusions The mentioned models can be used in remote places, in low- and middle-income countries, and laboratory equipment with limited resources to overcome a shortage of radiologists.

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