A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by...

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Vydané v:	Expert systems with applications Ročník 192; s. 116366
Hlavní autori:	Scarpiniti, Michele, Sarv Ahrabi, Sima, Baccarelli, Enzo, Piazzo, Lorenzo, Momenzadeh, Alireza
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Ltd 15.04.2022 Elsevier BV
Predmet:	Algorithms Anomaly detection Chest Computed tomography Computed Tomography (CT) Convolutional autoencoders Coronavirus disease 2019 Coronaviruses COVID-19 Deep denoising autoencoder Exploitation Feature learning Histograms Inspection Medical imaging Noise reduction Pneumonia Representations Viral diseases COVID-19 Pneumonia Convolutional autoencoders Computed Tomography (CT) Coronavirus disease 2019 Deep denoising autoencoder Feature learning Anomaly detection
ISSN:	0957-4174, 1873-6793, 0957-4174
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