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.
2023
9781643684048
9781643684055
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2982155