Rahaman, M., Li, C., Yao, Y., Kulwa, F., Rahman, M., Wang, Q., Qi, S., Kong, F., Zhu, X., Zhao, X. (2020) Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. Journal of X-Ray Science and Technology, 28 (5), 821-839. doi:10.3233/XST-200715
Identification of COVID-19 samples from chest X-Ray images using deep learning : a comparison of transfer learning approaches
|Author:||Rahaman, Md Mamunur1; Li, Chen1; Yao, Yudong2;|
1Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
2Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA
3Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
4Liaoning Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China
5Electrical Engineering Department, Pratt School of Engineering Duke University, Durham, NC, USA
6Whiting School of Engineering, Johns Hopkins University, 500 W University Parkway, MD, USA
7Environmental Engineering Department, Northeastern University, Shenyang, China
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020110288976
|Publish Date:|| 2020-11-02
Background: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system.
Objective: One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images.
Methods: Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task.
Results: A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively.
Conclusion: This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.
Journal of X-ray science and technology
|Pages:||821 - 839|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1182 Biochemistry, cell and molecular biology
3126 Surgery, anesthesiology, intensive care, radiology
This work was supported in part by the “National Natural Science Foundation of China” (No. 61806047), the “Fundamental Research Funds for the Central Universities” (No. N2019003, N2024005-2, N2019005), and the “China Scholarship Council” (No. 2018GBJ001757, 2017GXZ026396).
© 2020 – IOS Press and the authors. All rights reserved. This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).