About: Automated detection of COVID-19 cases using deep neural networks with X-ray images

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About: Automated detection of COVID-19 cases using deep neural networks with X-ray images Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Automated detection of COVID-19 cases using deep neural networks with X-ray images
Creator	Rajendra Acharya, U Ozturk, Tulin Talo, Muhammed Yildirim, Ozal Acharya, Rajendra Baran Baloglu, Ulas Yildirim, Azra
Source	Elsevier; Medline; PMC
abstract	Abstract The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
has issue date	2020-04-28(xsd:dateTime)
bibo:doi	10.1016/j.compbiomed.2020.103792
bibo:pmid	32568675
has license	els-covid
sha1sum (hex)	eba00e9c5c3cf024868d3866f82dabd2bb82a884
schema:url	https://doi.org/10.1016/j.compbiomed.2020.103792
resource representing a document's title	Automated detection of COVID-19 cases using deep neural networks with X-ray images
has PubMed Central identifier	PMC7187882
has PubMed identifier	32568675
schema:publication	Comput Biol Med
resource representing a document's body	covid:eba00e9c5c3cf024868d3866f82dabd2bb82a884#body_text
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