University of Sussex
Browse
COVIDQuantization1.pdf (2.57 MB)

Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification

Download (2.57 MB)
journal contribution
posted on 2023-06-10, 03:37 authored by Qinhua Hu, Francisco Nauber B Gois, Rafael Costa, Lijuan Zhang, Ling Yin, Naercio Magaia, Victor Hugo C de Albuquerque
The COVID-19 pandemic continues to wreak havoc on the world’s population’s health and well-being. Successful screening of infected patients is a critical step in the fight against it, with radiology examination using chest radiography being one of the most important screening methods. For the definitive diagnosis of COVID-19 disease, reverse-transcriptase polymerase chain reaction remains the gold standard. Currently available lab tests may not be able to detect all infected individuals; new screening methods are required. We propose a Multi-Input Transfer Learning COVID-Net fuzzy convolutional neural network to detect COVID-19 instances from torso X-ray, motivated by the latter and the open-source efforts in this research area. Furthermore, we use an explainability method to investigate several Convolutional Networks COVID-Net forecasts in an effort to not only gain deeper insights into critical factors associated with COVID-19 instances, but also to aid clinicians in improving screening. We show that using transfer learning and pre-trained models, we can detect it with a high degree of accuracy. Using X-ray images, we chose four neural networks to predict its probability. Finally, in order to achieve better results, we considered various methods to verify the techniques proposed here. As a result, we were able to create a model with an AUC of 1.0 and accuracy, precision, and recall of 0.97. The model was quantized for use in Internet of Things devices and maintained a 0.95 percent accuracy.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Applied Soft Computing

ISSN

1568-4946

Publisher

Elsevier

Volume

123

Page range

1-19

Article number

a108966

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-05-20

First Open Access (FOA) Date

2023-05-14

First Compliant Deposit (FCD) Date

2022-05-24

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC