About: Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays

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About: Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays Goto Sponge NotDistinct Permalink

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	Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays
Creator	Singh, Das, Kaur, Manjit Kumar, Naresh Kumar, Vijay Singh, Dilbag Das, Naresh Kaur, Vijay Kumar, Dilbag Kumar, Manjit Nripendra, Narayan
Source	Elsevier; Medline; PMC
abstract	The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models.
has issue date	2020-07-03(xsd:dateTime)
bibo:doi	10.1016/j.irbm.2020.07.001
bibo:pmid	32837679
has license	no-cc
sha1sum (hex)	98a52fc7a7b0dec567b107140f2d72d6c4c76d80
schema:url	https://doi.org/10.1016/j.irbm.2020.07.001
resource representing a document's title	Automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays
has PubMed Central identifier	PMC7333623
has PubMed identifier	32837679
schema:publication	Ing Rech Biomed
resource representing a document's body	covid:98a52fc7a7b0dec567b107140f2d72d6c4c76d80#body_text
is schema:about of	named entity 'chest X-rays' named entity 'Graphical' named entity 'infection' named entity 'COVID-19' named entity 'COVID-19' named entity 'transfer learning' named entity 'COVID' named entity 'chest x-ray' named entity 'chest X-rays' named entity 'tiredness' named entity 'CT scan' named entity 'X-rays' named entity 'Kaggle' named entity 'chest X-rays' named entity 'Wuhan' named entity 'chest x-ray' named entity 'softmax function' named entity 'sensitivity, specificity' named entity 'GoogleNet' named entity 'virus' named entity 'Softmax function' named entity 'deep learning' named entity 'ResNet' named entity 'chest X-rays' named entity 'chest X-rays' named entity 'COVID-19 testing' named entity 'activation function' named entity 'transfer learning' named entity 'deep learning' named entity 'f-measure' named entity 'coughing' named entity 'CT scan' named entity 'X-rays' named entity 'CNN' named entity 'COVID-19' named entity 'chest X-ray' named entity 'deep learning-based' named entity 'COVID-19' named entity 'infection' named entity 'deep learning-based' named entity 'computed tomography' named entity 'chest X-ray' named entity 'zoonotic' named entity 'infection' named entity 'CT scans' named entity 'f-measure' named entity 'chest CT scan' named entity 'neural network' named entity 'chest X-rays' named entity 'convolutional network' named entity 'cross-entropy' named entity 'COVID-19' named entity 'convolutional neural network' named entity 'chest x-ray' named entity 'X-rays' named entity 'infected person' named entity 'f-measure' named entity 'difficulty in breathing' named entity 'sensitivity, specificity' named entity 'Hubei Province' named entity 'X-rays' named entity 'f-measure' named entity 'convolution' named entity 'causality' named entity 'vaccines' named entity 'X-rays' named entity 'GoogleNet' named entity 'X-rays' named entity 'convolutional layer' named entity 'Wuhan' named entity 'COVID'

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