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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Expert systems with applications Jg. 192; S. 116366
Hauptverfasser:	Scarpiniti, Michele, Sarv Ahrabi, Sima, Baccarelli, Enzo, Piazzo, Lorenzo, Momenzadeh, Alireza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 15.04.2022 Elsevier BV
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!