About: Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks

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About: Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks 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	Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
Creator	Apostolopoulos, Ioannis Mpesiana, Tzani
Source	ArXiv; Medline; PMC
abstract	In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease. The aim of the study is to evaluate the performance of state-of-the-art convolutional neural network architectures proposed over the recent years for medical image classification. Specifically, the procedure called Transfer Learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The datasets utilized in this experiment are two. Firstly, a collection of 1427 X-ray images including 224 images with confirmed Covid-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 images of normal conditions. Secondly, a dataset including 224 images with confirmed Covid-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions. The data was collected from the available X-ray images on public medical repositories. The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively. Since by now, all diagnostic tests show failure rates such as to raise concerns, the probability of incorporating X-rays into the diagnosis of the disease could be assessed by the medical community, based on the findings, while more research to evaluate the X-ray approach from different aspects may be conducted.
has issue date	2020-04-03(xsd:dateTime)
bibo:doi	10.1007/s13246-020-00865-4
bibo:pmid	32524445
has license	no-cc
sha1sum (hex)	50e92d0dc7bc607ee56cd834348d4f981c2c745d
schema:url	https://doi.org/10.1007/s13246-020-00865-4
resource representing a document's title	Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
has PubMed Central identifier	PMC7118364
has PubMed identifier	32524445
schema:publication	Phys Eng Sci Med
resource representing a document's body	covid:50e92d0dc7bc607ee56cd834348d4f981c2c745d#body_text
is schema:about of	named entity 'collection' named entity 'study' named entity 'images' named entity 'architectures' named entity 'diagnosis' named entity 'common' named entity 'data' named entity 'FAILURE' named entity 'SECONDLY' named entity 'CORONAVIRUS DISEASE' named entity 'IMAGE CLASSIFICATION' named entity 'TARGET' named entity 'sensitivity' named entity 'failure rates' named entity 'images' named entity 'bacterial pneumonia' named entity 'evaluate' named entity 'transfer learning' named entity 'normal' named entity 'Covid-19' named entity 'X-ray' named entity 'raise' named entity 'datasets' named entity 'patients' named entity 'medical' named entity 'image classification' named entity 'incorporating' named entity 'convolutional neural network' named entity 'state-of-the-art' named entity 'X-ray' named entity 'Covid-19' named entity 'X-ray images' named entity 'image classification' named entity 'medical image' named entity 'convolutional neural networks' named entity 'transfer learning' named entity 'assessed' named entity 'disease' named entity 'collected data' named entity 'CNN' named entity 'biomarkers' named entity 'CNN' named entity 'deep learning' named entity 'Machine learning' named entity 'deep learning' named entity 'CNN' named entity 'CNNs' named entity 'transfer learning' named entity 'pneumonia' named entity 'large-scale' named entity 'small sample' named entity 'variance' named entity 'CNNs' named entity 'CNN' named entity 'Covid' named entity 'CNNs' named entity 'bacterial pneumonia' named entity 'medical images' named entity 'recognize patterns' named entity 'CNN' named entity 'False Positives' named entity 'image classification' named entity 'pneumonia' named entity 'Neural Network' named entity 'transfer learning' named entity 'sensitivity, and specificity' named entity 'X-ray images' named entity 'Covid-19' named entity 'Covid-19 disease' named entity 'pneumonia' named entity 'virus' named entity 'pneumonia' named entity 'transfer learning' named entity 'pneumonia' named entity 'X-rays' named entity 'pneumonia' named entity 'CNNs' named entity 'Covid' named entity 'X-rays' named entity 'deep learning'

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