16
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based corona detection method using lung X-ray image

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, we proposed a novel intelligent computer vision method to automatically detect the Covid-19 virus. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called as Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN) and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV) and 10-fold cross-validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that we reached the perfect classification rate by using X-ray image for Covid-19 detection.

          Highlights

          • A novel iterative feature selection method is presented.

          • A novel Covid-19 detection method is presented by using Residual Exemplar LBP and iterative ReliefF.

          • The proposed method achieved 100.0% classification accuracy for Covid-19 detection by using lung X-ray images.

          Related collections

          Most cited references28

          • Record: found
          • Abstract: found
          • Article: found

          World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)

          An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak

            Coronavirus disease (COVID-19) is caused by SARS-COV2 and represents the causative agent of a potentially fatal disease that is of great global public health concern. Based on the large number of infected people that were exposed to the wet animal market in Wuhan City, China, it is suggested that this is likely the zoonotic origin of COVID-19. Person-to-person transmission of COVID-19 infection led to the isolation of patients that were subsequently administered a variety of treatments. Extensive measures to reduce person-to-person transmission of COVID-19 have been implemented to control the current outbreak. Special attention and efforts to protect or reduce transmission should be applied in susceptible populations including children, health care providers, and elderly people. In this review, we highlights the symptoms, epidemiology, transmission, pathogenesis, phylogenetic analysis and future directions to control the spread of this fatal disease.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses

              Graphical abstract
                Bookmark

                Author and article information

                Contributors
                Journal
                Chemometr Intell Lab Syst
                Chemometr Intell Lab Syst
                Chemometrics and Intelligent Laboratory Systems
                Elsevier B.V.
                0169-7439
                0169-7439
                18 May 2020
                18 May 2020
                : 104054
                Affiliations
                [a ]Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
                [b ]Department of Software Engineering, College of Engineering, Firat University, Elazig, Turkey
                Author notes
                []Corresponding author. turkertuncer@ 123456firat.edu.tr
                Article
                S0169-7439(20)30197-0 104054
                10.1016/j.chemolab.2020.104054
                7233238
                32427226
                dcab25b4-f055-48c3-8f6c-eba3cb6aeeb3
                © 2020 Elsevier B.V. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 25 March 2020
                : 1 May 2020
                : 12 May 2020
                Categories
                Article

                residual exemplar lbp,covid-19,iterative relieff,classification,machine learning

                Comments

                Comment on this article