Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.
J Xray Sci Technol
; 28(5): 821-839, 2020.
Article
in English
| MEDLINE | ID: covidwho-709463
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Tomography, X-Ray Computed
/
Coronavirus Infections
/
Deep Learning
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Xray Sci Technol
Journal subject:
Radiology
Year:
2020
Document Type:
Article
Affiliation country:
XST-200715
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