RESEARCH ARTICLE
Novel Multi-Modal Throat Inflammation and Chest Radiography based Early-Diagnosis and Mass-Screening of COVID-19
Ojas A. Ramwala1, *, Poojan Dalal2, Parima Parikh3, Upena Dalal1, Mita C. Paunwala4, Chirag. N. Paunwala2
Article Information
Identifiers and Pagination:
Year: 2021Volume: 15
Issue: Suppl-2, M11
First Page: 226
Last Page: 234
Publisher ID: TOBEJ-15-226
DOI: 10.2174/1874120702115010226
Article History:
Received Date: 13/1/2021Revision Received Date: 01/9/2021
Acceptance Date: 16/11/2021
Electronic publication date: 31/12/2021
Collection year: 2021
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
The upsurge of COVID-19 has received significant international contemplation considering its life-threatening ramifications. To ensure that the susceptible patients can be quarantined to control the spread of the disease during the incubation period of the coronavirus, it becomes imperative to automatically and non-invasively mass screen patients. The diagnosis using RT-PCR is arduous and time-consuming. Currently, the non-invasive mass screening of susceptible cases is being performed by utilizing the thermal screening technique. However, with the consumption of paracetamol, the symptoms of fever can be suppressed.
Methods:
A novel multi-modal approach has been proposed. Throat inflammation-based mass screening and early prediction followed by Chest X-Ray based diagnosis have been proposed. Depth-wise separable convolutions have been utilized by fine-tuning Xception Net and Mobile Net architectures. NADAM optimizer has been leveraged to promote faster convergence.
Results:
The proposed method achieved 91% accuracy on the throat inflammation identification task and 96% accuracy on chest radiography conducted on the dataset.
Conclusion:
Evaluation of the proposed method indicates promising results and henceforth validates its clinical reliability. The future direction could be working on a larger dataset in close collaboration with the medical fraternity.
