A hybrid rexception network for COVID-19 classification from chest X-ray images

Nour Aburaed, Mina Al-Saad, Alavikunhu Panthakkan, Saeed al Mansoori, Hussain Al-Ahmad, Stephen Marshall

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Abstract

Nowadays, with the rapid spread of Coronavirus disease (COVID-19) across the globe, the necessity to develop an intelligent system for early diagnosis and detection the COVID-19 infectious disease increases. In recent researches, Chest Xray (CXR) of individual lungs became a common method to identify COVID-19 virus. Manual interpretation of the CXR images can be a lengthy process and subjective to human errors. In this paper, a hybrid Deep Learning model called ReXception is implemented, trained, and evaluated using two types of datasets; Mutliclass and Binary. The network is evaluated based on its overall accuracy, loss, precision, and recall, in addition to the running time and network size. The results show positive indications of the network's performance, especially when compared to other state-of-the-art networks.

Original languageEnglish
Title of host publication2021 28th IEEE International Conference on Electronics, Circuits, and Systems, (ICECS), 2021
Place of PublicationPiscataway, N.J.
Pages1-5
Number of pages5
ISBN (Electronic)9781728182810
DOIs
Publication statusPublished - 10 Jan 2022
Event28th IEEE International Conference on Electronics, Circuits, and Systems - Dubai, United Arab Emirates
Duration: 28 Nov 20211 Dec 2021

Publication series

Name2021 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Proceedings

Conference

Conference28th IEEE International Conference on Electronics, Circuits, and Systems
Abbreviated titleICECS 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period28/11/211/12/21

Keywords

  • chest X-ray
  • classification
  • convolutional neural network (CNN)
  • Covid-19
  • deep learning (DL)

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