Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification

•Design a novel Unsupervised DL model for COVID-19 Diagnosis and Classification•Propose a Parameter Tuned Adagrad with Inception v4 model as feature extractor•Employ an Unsupervised Variational Autoencoder Model for classification process•Validate the classification performance on COVID Chest X-ray...

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Vydané v:	Pattern recognition letters Ročník 151; s. 267 - 274
Hlavní autori:	Mansour, Romany F., Escorcia-Gutierrez, José, Gamarra, Margarita, Gupta, Deepak, Castillo, Oscar, Kumar, Sachin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.11.2021 Elsevier Science Ltd
Predmet:	Artificial intelligence Classification Computer vision Coronaviruses COVID-19 Deep learning Experimentation Feature extraction Image classification Image enhancement Image quality Learning algorithms Machine learning Unsupervised learning Variational autoencoder Wiener filtering COVID-19 Deep learning Variational autoencoder Unsupervised learning Image classification
ISSN:	0167-8655, 1872-7344, 0167-8655
On-line prístup:	Získať plný text
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