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Article Title

COVID-19 Detection from Chest X-ray Images using CNNs Models: Further Evidence from Deep Transfer Learning

Authors

Mohamed Samir Boudrioua, Ronin instituteFollow

Abstract

Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using Deep Learning.

Methods: We collect a relatively large COVID-19 dataset comparing with previous studies that contain 309 real COVID-19 chest x-ray images. We also prepare 2,000 chest x-ray images of pneumonia cases and 1,000 images of healthy chest cases. Deep Transfer Learning is used to detect abnormalities in our image dataset. We fine-tune three, pre-trained convolutional neural networks (CNNs) models on a training dataset: DenseNet 121, NASNetLarge, and NASNetMobile.

Results: The evaluation of our models on a test dataset show that these models achieve an average sensitivity rate of approximately 99.45 % and an average specificity rate of approximately 99.5 %.

Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using Deep Transfer Learning. However, the clinical diagnosis of COVID-19 disease is always necessary.

Funder

The author received no specific funding for this work.

DOI

10.18297/jri/vol4/iss1/53

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Boudrioua, Mohamed Samir (2020) "COVID-19 Detection from Chest X-ray Images using CNNs Models: Further Evidence from Deep Transfer Learning," The University of Louisville Journal of Respiratory Infections: Vol. 4 : Iss. 1 , Article 53.
DOI: 10.18297/jri/vol4/iss1/53
Available at: https://ir.library.louisville.edu/jri/vol4/iss1/53