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Advanced Cognitive Algorithm for Biomedical Data Processing: COVID-19 Pattern Recognition as a Case Study.
Elhoseny, Mohamed; Tarek, Zahraa; El-Hasnony, Ibrahim M.
  • Elhoseny M; College of Computing and Informatics, University of Sharjah, Sharjah, UAE.
  • Tarek Z; Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
  • El-Hasnony IM; Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
J Healthc Eng ; 2022: 1773259, 2022.
Article in English | MEDLINE | ID: covidwho-1775006
ABSTRACT
Automated disease prediction has now become a key concern in medical research due to exponential population growth. The automated disease identification framework aids physicians in diagnosing disease, which delivers accurate disease prediction that provides rapid outcomes and decreases the mortality rate. The spread of Coronavirus disease 2019 (COVID-19) has a significant effect on public health and the everyday lives of individuals currently residing in more than 100 nations. Despite effective attempts to reach an appropriate trend to forecast COVID-19, the origin and mutation of the virus is a crucial obstacle in the diagnosis of the detected cases. Even so, the development of a model to forecast COVID-19 from chest X-ray (CXR) and computerized tomography (CT) images with the correct decision is critical to assist with intelligent detection. In this paper, a proposed hybrid model of the artificial neural network (ANN) with parameters optimization by the butterfly optimization algorithm has been introduced. The proposed model was compared with the pretrained AlexNet, GoogLeNet, and the SVM to identify the publicly accessible COVID-19 chest X-ray and CT images. There were six datasets for the examinations three datasets with X-ray pictures and three with CT images. The experimental results approved the superiority of the proposed model for cognitive COVID-19 pattern recognition with average accuracy 90.48, 81.09, 86.76, and 84.97% for the proposed model, support vector machine (SVM), AlexNet, and GoogLeNet, respectively.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Case report / Prognostic study Limits: Humans Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Case report / Prognostic study Limits: Humans Language: English Journal: J Healthc Eng Year: 2022 Document Type: Article