Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Computer Aid Screening of COVID19 using X-ray and CT Scan Images: A Comparative Study

Version 1 : Received: 19 August 2020 / Approved: 21 August 2020 / Online: 21 August 2020 (05:17:46 CEST)

How to cite: Sethy, P.K.; Pandey, C.; Behera, S.K. Computer Aid Screening of COVID19 using X-ray and CT Scan Images: A Comparative Study. Preprints 2020, 2020080472 (doi: 10.20944/preprints202008.0472.v1). Sethy, P.K.; Pandey, C.; Behera, S.K. Computer Aid Screening of COVID19 using X-ray and CT Scan Images: A Comparative Study. Preprints 2020, 2020080472 (doi: 10.20944/preprints202008.0472.v1).

Abstract

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

Subject Areas

Computer-aided Screening; Coronavirus; X-Ray; CT scan; Machine Learning; Transfer Learning; Deep Learning

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.