A Novel Deep Learning and Ensemble Learning Mechanism for Delta-Type Covid-19 Detection

Bibliographic Data

ID22082045
AuthorsHabib Ullah Khan (0000-0001-8373-2781, corresponding author), Sulaiman Khan (0000-0003-2562-3477), Shah Nazir (0000-0003-0126-9944, University of Swabi)
Year2022
Volume10
Pages875971-875971
Publication date2022-07-08
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2022.875971
PMID35874982
OpenAlexW4284896636
LanguageEN
References cited23

Recently, the novel coronavirus disease 2019 (COVID-19) has posed many challenges to the research community by presenting grievous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that results in a huge number of mortalities and high morbidities worldwide. Furthermore, the symptoms-based variations in virus type add new challenges for the research and practitioners to combat. COVID-19-infected patients comprise trenchant radiographic visual features, including dry cough, fever, dyspnea, fatigue, etc. Chest X-ray is considered a simple and non-invasive clinical adjutant that performs a key role in the identification of these ocular responses related to COVID-19 infection. Nevertheless, the defined availability of proficient radiologists to understand the X-ray images and the elusive aspects of disease radiographic replies to remnant the biggest bottlenecks in manual diagnosis. To address these issues, the proposed research study presents a hybrid deep learning model for the accurate diagnosing of Delta-type COVID-19 infection using X-ray images. This hybrid model comprises visual geometry group 16 (VGG16) and a support vector machine (SVM), where the VGG16 is accustomed to the identification process, while the SVM is used for the severity-based analysis of the infected people. An overall accuracy rate of 97.37% is recorded for the assumed model. Other performance metrics such as the area under the curve (AUC), precision, F-score, misclassification rate, and confusion matrix are used for validation and analysis purposes. Finally, the applicability of the presumed model is assimilated with other relevant techniques. The high identification rates shine the applicability of the formulated hybrid model in the targeted research domain

Confusion · Confusion matrix · Deep learning · Disease · Machine learning · Pathology · Radiography · Radiology · Support vector machine · Computer Science · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI · Dental Research and COVID-19 · Medicine · Psychology · Artificial Intelligence

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