TY - JOUR
T1 - Identification of COVID-19 samples from chest X-Ray images using deep learning
T2 - A comparison of transfer learning approaches
AU - Rahaman, Md Mamunur
AU - Li, Chen
AU - Yao, Yudong
AU - Kulwa, Frank
AU - Rahman, Mohammad Asadur
AU - Wang, Qian
AU - Qi, Shouliang
AU - Kong, Fanjie
AU - Zhu, Xuemin
AU - Zhao, Xin
N1 - Publisher Copyright:
© 2020 - IOS Press and the authors. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - COVID-19
KW - Chest X-Ray Image
KW - image identification
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85091890721&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091890721&partnerID=8YFLogxK
U2 - 10.3233/XST-200715
DO - 10.3233/XST-200715
M3 - Article
C2 - 32773400
AN - SCOPUS:85091890721
SN - 0895-3996
VL - 28
SP - 821
EP - 839
JO - Journal of X-Ray Science and Technology
JF - Journal of X-Ray Science and Technology
IS - 5
ER -