Author: Sebdani, A. M.; Mostafavi, A.
Title: Medical Image Processing and Deep Learning to Diagnose COVID-19 with CT Images Cord-id: j6usw72c Document date: 2021_1_1
ID: j6usw72c
Snippet: In this article two classification algorithms of computed tomography images with the purpose of detection of COVID-19 based on the local binary pattern, the GLCM features and the other extraction of statistical has been proposed to classify the chest images into two classes, COVID-19 patient or non-COVID-19 person. For this purpose, 746 images from the lung of healthy people and with the positive symptoms of COVID-19 disorder from the public data were collected and the collection of the features
Document: In this article two classification algorithms of computed tomography images with the purpose of detection of COVID-19 based on the local binary pattern, the GLCM features and the other extraction of statistical has been proposed to classify the chest images into two classes, COVID-19 patient or non-COVID-19 person. For this purpose, 746 images from the lung of healthy people and with the positive symptoms of COVID-19 disorder from the public data were collected and the collection of the features that was extracted in gray level was the input for artificial neural network (ANN) and support vector machine classifier (SVM), and the result of models shows that the highest accuracy is for SVM 98.5% and 97.2% accuracy by using the ANN. © 2021 IEEE.
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