Identification of COVID-19 samples from chest X-ray images using deep learning : a comparison of transfer learning approaches
Rahaman, Md Mamunur; Li, Chen; Yao, Yudong; Kulwa, Frank; Rahman, Mohammad Asadur; Wang, Qian; Qi, Shouliang; Kong, Fanjie; Zhu, Xuemin; Zhao, Xin (2020-09-19)
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
© 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).
https://creativecommons.org/licenses/by-nc/4.0/
https://urn.fi/URN:NBN:fi-fe2020110288976
Tiivistelmä
Abstract
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.
Kokoelmat
- Avoin saatavuus [31941]