Abstract

    Open Access Research Article Article ID: ACMPH-7-254

    A modified CNN-based Covid-19 detection using CXR

    Mohammad Hossein Amini*1, Mohammad Bagher Menhaj and Heidar Ali Talebi

    In this paper, a deep neural network for the purpose of detecting COVID-19 from Chest X-Ray (CXR) images is presented. Since this pandemic has emerged worldwide , there is no large dataset available for it. So for its detection, care must be taken not to use methods with high variance. However, for a deep neural network to get acceptable performance, we usually need huge amounts of datasets. Otherwise, there may be issues like overfitting. To resolve this problem, we use the beautiful idea of transfer learning. Training a deep neural network with the idea of transfer learning on 2 available datasets on the web, we achieved a COVID-19 detection accuracy of 98% on about 1000 test samples.

    1(Use footnote for providing further information about author (webpage, alternative address)—not for acknowledging funding agencies.)

    Keywords:

    Published on: Jul 24, 2021 Pages: 142-145

    Full Text PDF Full Text HTML DOI: 10.17352/2455-5479.000154
    CrossMark Publons Harvard Library HOLLIS Search IT Semantic Scholar Get Citation Base Search Scilit OAI-PMH ResearchGate Academic Microsoft GrowKudos Universite de Paris UW Libraries SJSU King Library SJSU King Library NUS Library McGill DET KGL BIBLiOTEK JCU Discovery Universidad De Lima WorldCat VU on WorldCat

    Global Views

    Case Reports

    Peertechz Tweets

    Pinterest on ACMPH