About: Efficient Deep Network Architecture for COVID-19 Detection Using Computed Tomography Images

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Efficient Deep Network Architecture for COVID-19 Detection Using Computed Tomography Images
Creator	Kumar, Abhimanyu Dubey, Satish Goel, Chirag Srivastava, Vishal
Source	MedRxiv
abstract	Globally the devastating consequence of COVID-19 or Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2) has posed danger on the life of living beings. Doctors and scientists throughout the world are working day and night to combat the proliferation or transmission of this deadly disease in terms of technology, finances, data repositories, protective equipment, and many other services. Rapid and efficient detection of COVID-19 reduces the rate of spreading this deadly disease and early treatment improve the recovery rate. In this paper, we proposed a new framework to exploit powerful features extracted from the autoencoder and Gray Level Co-occurence Matrix (GLCM), combined with random forest algorithm for the efficient and fast detection of COVID-19 using computed tomographic images. The model's performance is evident from its 97.78% accuracy, 96.78% recall, and 98.77% specificity.
has issue date	2020-08-17(xsd:dateTime)
bibo:doi	10.1101/2020.08.14.20170290
has license	medrxiv
sha1sum (hex)	d5ca3cfe247aa245e54a837dd207e5d9d722f078
schema:url	https://doi.org/10.1101/2020.08.14.20170290
resource representing a document's title	Efficient Deep Network Architecture for COVID-19 Detection Using Computed Tomography Images
resource representing a document's body	covid:d5ca3cfe247aa245e54a837dd207e5d9d722f078#body_text
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