COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images.
J Healthc Eng
; 2021: 6658058, 2021.
Article
in English
| MEDLINE | ID: covidwho-1277017
ABSTRACT
The COVID-19 pandemic has a significant negative effect on people's health, as well as on the world's economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers' attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Radiography, Thoracic
/
Radiographic Image Interpretation, Computer-Assisted
/
Deep Learning
/
SARS-CoV-2
/
COVID-19
Type of study:
Diagnostic study
Limits:
Humans
Language:
English
Journal:
J Healthc Eng
Year:
2021
Document Type:
Article
Affiliation country:
2021
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