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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

MXRFCS : Development of an enhanced multidomain X-ray feature representation model for the detection and classification of COVID severity levels

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pp. 369–382Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1262XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1262
Pages:
369–382

Abstract

Global healthcare has been under stress due to the COVID-19 pandemic, necessitating effective methods to determine COVID-19 patient severity and distribute resources accordingly. Current COVID-19 X-ray models are either too complicated or too ineffective to employ in real time. This paper introduces a novel method for assessing COVID-19 severity using an enhanced multimodal X-ray model. A strong patient representation is produced by combining demographics, clinical information, and X-ray results. Multidomain features, including frequency, Gabor, Wavelet, and entropy components, are produced from this data. Using this model, a customized neural network is trained to predict COVID-19 severity levels. A dataset of X-ray pictures and clinical information from COVID-19 patients were used to evaluate the model. The suggested model achieves 98.5% accuracy on several datasets, outperforming existing approaches in the identification of COVID-19 severity levels, according to the results. Performance shows promise in terms of accuracy, memory, and latency, supporting early and efficient management of severe COVID-19 cases and thwarting the pandemic without clinical usage.

Keywords

Subject Classifications

Primary 93A30Secondary 49K15

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