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Home > Archives > Volume 20, No 5 (2022) > Article

DOI: 10.14704/nq.2022.20.5.NQ22192

An Automated System for the Classification of COVID-19, Suspected COVID-19 and Healthy Lung CT Images based on Local Binary Pattern and Deep Learning Features

Luma J. Satoory, Hussain S. Hasan and Ali M. Hasan

Abstract

Because of the inadequate capacity and a substantial surge of probable COVID-19 cases, several health systems around worldwide have collapsed. As a result, the requirement for a rapid, effective, and precise way to reduce radiologists' workload in diagnosing suspected instances has arisen. The goal of the present study is to develop a novel system to automatically diagnose and classify lung CT scans into three categories: suspected covid-19, covid-19, and healthy lung scans. Before feature extraction using convolutional neural network (CNN) and Local Binary Pattern (LBP) approaches, the CT scans are first pre-processed through implementing a set of algorithms. Lastly, with the use of the support vector machine (SVM) model, such features are divided into three groups. The maximum accuracy attained in classifying a dataset of 351 CT scans of the lungs was 98.22%. The outcomes of the experiments show that merging the extracted features increases the effectiveness of lung classification CT scans.

Keywords

Deep Learning, COVID-19, LBP, CNN, CT Lung Scans, SVM Classifier.

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