Anglia Ruskin Research Online (ARRO)
Browse

File(s) not publicly available

Deep Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images using ResNetV2

conference contribution
posted on 2023-07-26, 15:54 authored by T. Rakhymzhan, Javad Zarrin, Mahdi Maktab Dar Oghaz, Lakshmi Babu Saheer
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.

History

Page range

106-116

ISSN

978-3-031-10464-0

ISBN

978-3-031-10463-3

Conference proceeding

Intelligent Computing

Name of event

SAI Computing Conference 2022

Location

Online

Event start date

2022-07-14

Event finish date

2022-07-15

Language

  • other

Legacy posted date

2022-06-13

Legacy Faculty/School/Department

COVID-19 Research Collection

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC