1. Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches
- Author
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Xin Zhao, Frank Kulwa, Mohammad Asadur Rahman, Shouliang Qi, Chen Li, Mamunur Rahaman, Qian Wang, Fanjie Kong, Yu-Dong Yao, and Xuemin Zhu
- Subjects
image identification ,Coronavirus disease 2019 (COVID-19) ,Databases, Factual ,Computer science ,Pneumonia, Viral ,02 engineering and technology ,transfer learning ,030218 nuclear medicine & medical imaging ,Diagnosis, Differential ,03 medical and health sciences ,Betacoronavirus ,0302 clinical medicine ,Deep Learning ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Radiology, Nuclear Medicine and imaging ,Electrical and Electronic Engineering ,Instrumentation ,Chest X-Ray Image ,Pandemics ,Radiation ,business.industry ,SARS-CoV-2 ,Deep learning ,Critical factors ,COVID-19 ,Reproducibility of Results ,Pattern recognition ,Pneumonia ,Condensed Matter Physics ,Identification (information) ,Benchmark (computing) ,X ray image ,020201 artificial intelligence & image processing ,Radiography, Thoracic ,Artificial intelligence ,Neural Networks, Computer ,F1 score ,business ,Transfer of learning ,Coronavirus Infections ,Tomography, X-Ray Computed ,Algorithms ,Research Article - 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.
- Published
- 2020