Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison.
J Xray Sci Technol
; 29(2): 197-210, 2021.
Article
in English
| MEDLINE | ID: covidwho-1045526
ABSTRACT
The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Radiographic Image Interpretation, Computer-Assisted
/
COVID-19
Type of study:
Diagnostic study
Limits:
Humans
Language:
English
Journal:
J Xray Sci Technol
Journal subject:
Radiology
Year:
2021
Document Type:
Article
Affiliation country:
XST-200784
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