Coronavirus can lead to respiratory illnesses ranging from mild to severe, and even death, which makes early detection critical. However, current COVID-19 (Coronavirus Disease 2019) detection methods are not only expensive but also time-consuming. This poses a challenge, especially with an increasing number of patients and demand for testing kits. Waiting for test results for a few days is not ideal, as the outbreak can spread quickly in the meantime. To address this issue, we propose a COVID-19 prediction infrastructure using deep learning. This innovative android-based application uses a Convolutional Neural Network model, trained on a custom dataset with an accuracy of 97 percent, to predict whether COVID-19 is present or not. With this fast and low-cost approach, users can quickly detect COVID-19 and take appropriate actions to reduce the risk of transmission.
COVID-19 Prediction Infrastructure Using Deep Learning / Abbas, Zahra; Fiorino, Mario; Naqi, Syed Muhammad; Abbas, Musarat. - ELETTRONICO. - 32:(2023), pp. 125-134. (Intervento presentato al convegno 19th International Conference on Intelligent Environments tenutosi a Mauritius nel 27 – 30 June 2023) [10.3233/AISE230020].
COVID-19 Prediction Infrastructure Using Deep Learning
Fiorino, Mario;
2023
Abstract
Coronavirus can lead to respiratory illnesses ranging from mild to severe, and even death, which makes early detection critical. However, current COVID-19 (Coronavirus Disease 2019) detection methods are not only expensive but also time-consuming. This poses a challenge, especially with an increasing number of patients and demand for testing kits. Waiting for test results for a few days is not ideal, as the outbreak can spread quickly in the meantime. To address this issue, we propose a COVID-19 prediction infrastructure using deep learning. This innovative android-based application uses a Convolutional Neural Network model, trained on a custom dataset with an accuracy of 97 percent, to predict whether COVID-19 is present or not. With this fast and low-cost approach, users can quickly detect COVID-19 and take appropriate actions to reduce the risk of transmission.| File | Dimensione | Formato | |
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| COVID_19_Prediction_Infrastructure_using_Deep_Learning.pdf accesso aperto 
											Descrizione: Paper : COVID-19 Prediction Infrastructure Using Deep Learning
										 
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											2. Post-print / Author's Accepted Manuscript
										 
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| AISE-32-AISE230020.pdf accesso aperto 
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											2a Post-print versione editoriale / Version of Record
										 
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https://hdl.handle.net/11583/2982155
			
		
	
	
	
			      	