Deep convolutional neural networks for COVID-19 detection from chest X-Ray images using ResNetV2

Tomiris Rakhymzhan, Javad Zarrin*, Mahdi Maktab-Dar-Oghaz, Lakshmi Babu Saheer

*Corresponding author for this work

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

Abstract

COVID-19 has been identified as a highly contagious and rapidly spreading disease around the world. The high infection and mortality rate characterizes this as a very dangerous disease and has been marked as a global pandemic by the world health organization. Existing COVID-19 testing methods, such as RT-PCR are not completely reliable or convenient. Since the virus affects the respiratory tract, manual analysis of chest X-rays could be a more reliable but not convenient or scalable testing technique. Hence, there is an urgent need for a faster, cheaper, and automated way of detecting the presence of the virus by automatically analyzing chest X-ray images using deep learning algorithms. ResNetV2 is one of the pre-trained deep convolutional neural network models that could be explored for this task. This paper aims to utilize the ResNetV2 model for the detection of COVID-19 from chest X-ray images to maximize the performance of this task. This study performs fine-tuning of ResNetV2 networks (specifically, ResNet101V2), which is performed in two main stages: firstly, training model with frozen ResNetV2 base layers, and secondly, unfreezing some layers of the ResNetV2 and retraining with a lower learning rate. Model fine-tuned on ResNet101V2 shows competitive and promising results with 98.50% accuracy and 97.24% sensitivity.
Original languageEnglish
Title of host publicationIntelligent Computing
Subtitle of host publicationProceedings of the 2022 Computing Conference
EditorsKohei Arai
Place of PublicationCham
PublisherSpringer
Pages106-116
Number of pages11
Volume2
ISBN (Electronic)9783031104640
ISBN (Print)9783031104633
DOIs
Publication statusPublished - 7 Jul 2022
Externally publishedYes
EventComputing Conference 2022 - Virtual, United Kingdom
Duration: 14 Jul 202215 Jul 2022
https://saiconference.com/Computing

Publication series

NameLecture Notes in Networks and Systems
Volume507 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceComputing Conference 2022
Country/TerritoryUnited Kingdom
Period14/07/2215/07/22
OtherComputing Conference (formerly called Science and Information (SAI) Conference) is a research conference held in London, UK since 2013.
Internet address

Keywords

  • Convolutional neural networks
  • Covid19
  • ResNet
  • Fine-tuning

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