ABSTRACT
COVID-19 has roused the scientific community, prompting calls for immediate solutions to avoid the infection or at least reduce the virus's spread. Despite the availability of several licensed vaccinations to boost human immunity against the disease, various mutated strains of the virus continue to emerge, posing a danger to the vaccine's efficacy against new mutations. As a result, the importance of the early detection of COVID-19 infection becomes evident. Cough is a prevalent symptom in all COVID-19 mutations. Unfortunately, coughing can be a symptom of various of diseases, including pneumonia and infiuenza. Thus, identifying the coughing behavior might help clinicians diagnose the COVID-19 infection earlier and distinguish coronavirus-induced from non-coronavirus-induced coughs. From this perspective, this research proposes a novel approach for diagnosing COVID-19 infection based on cough sound. The main contributions of this study are the encoding of cough behavior, the investigation of its unique characteristics, and the representation of these traits as association rules. These rules are generated and distinguished with the help of data mining and machine learning techniques. Experiments on the Virufy COVID-19 open cough dataset reveal that cough encoding can provide the desired accuracy (100%). © 2023, ECTI Association. All rights reserved.
ABSTRACT
COVID-19 is a vital zoonotic illness caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). COVID-19 is a very wide-spread among humans thus the early detection and curing of the disease offers a high opportunity of survival for patients. Computed Tomography (CT) plays an important role in the diagnosis of COVID-19. As chest radiography can give an indicator of coronavirus. Though, an automated Computer Aided Diagnostic (CAD) system for COVID-19 based on chest X-Ray image analysis is presented in this article. It is designed for COVID-19 recognition from other MERS, SARS, and ARDS viral pneumonia. The optimal threshold value for the segmentation of a chest image is deduced by exploiting Li s' method and particle swarm intelligence. Laws' masks are then applied to the segmented chest image for secondary characteristics highlighting. After that, nine different vectors of attributes are extracted from the Grey Level Co-occurrence Matrix (GLCM) representation of each Law's mask result. Support vector machine ensemble models are then built based on the extracted feature vectors. Finally, a weighted voting method is utilized to combine the decisions of ensemble classifiers. Experimental findings show an accuracy of 98.04 %. It indicates that the suggested CAD scheme can be a promising supplementary COVID-19 diagnostic tool for clinical doctors. © 2020, Intelligent Network and Systems Society.