• Md Mamunur Rahaman, Chen Li, Yudong Yao, Frank Kulwa, Mohammad Asadur Rahman, Qian Wang, Shouliang Qi, Fanjie Kong, Xuemin Zhu, and Xin Zhao.
• Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
• J Xray Sci Technol. 2020 Jan 1; 28 (5): 821-839.

BackgroundThe 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.ObjectiveOne 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.MethodsDue 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.ResultsA 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.ConclusionThis study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

28 keywords...

hide…