Md Mamunur Rahaman1, Chen Li1, Yudong Yao2, Frank Kulwa1, Mohammad Asadur Rahman3, Qian Wang4, Shouliang Qi1, Fanjie Kong5, Xuemin Zhu6, Xin Zhao7. 1. Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China. 2. Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA. 3. Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland. 4. Liaoning Hospital and Institute, Cancer Hospital of China Medical University, Shenyang, China. 5. Electrical Engineering Department, Pratt School of Engineering Duke University, Durham, NC, USA. 6. Whiting School of Engineering, Johns Hopkins University, 500 W University Parkway, MD, USA, USA. 7. Environmental Engineering Department, Northeastern University, Shenyang, China.
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.
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infectedpeople 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.
Entities:
Keywords:
COVID-19; Chest X-Ray Image; image identification; transfer learning
Authors: Adam Bernheim; Xueyan Mei; Mingqian Huang; Yang Yang; Zahi A Fayad; Ning Zhang; Kaiyue Diao; Bin Lin; Xiqi Zhu; Kunwei Li; Shaolin Li; Hong Shan; Adam Jacobi; Michael Chung Journal: Radiology Date: 2020-02-20 Impact factor: 11.105
Authors: Kaoutar Ben Ahmed; Gregory M Goldgof; Rahul Paul; Dmitry B Goldgof; Lawrence O Hall Journal: IEEE Access Date: 2021-05-13 Impact factor: 3.367