Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches

Autor: Xin Zhao, Frank Kulwa, Mohammad Asadur Rahman, Shouliang Qi, Chen Li, Mamunur Rahaman, Qian Wang, Fanjie Kong, Yu-Dong Yao, Xuemin Zhu
Jazyk: angličtina
Rok vydání: 2020
Předmět:
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
Zdroj: Journal of X-Ray Science and Technology
ISSN: 1095-9114
0895-3996
Popis: 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.
Databáze: OpenAIRE
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