About: Preliminary Results on Pulmonary Tuberculosis Detection in Chest X-Ray Using Convolutional Neural Networks

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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
title	Preliminary Results on Pulmonary Tuberculosis Detection in Chest X-Ray Using Convolutional Neural Networks
Creator	Alves, Domingos Bernardi, Filipe Eloi, Márcio Filho, Colombo Kritski, Afrânio Laura De Oliveira, Lariza Mello Galliez, Rafael Santos, Marcel
source	PMC
abstract	Tuberculosis (TB), is an ancient disease that probably affects humans since pre-hominids. This disease is caused by bacteria belonging to the mycobacterium tuberculosis complex and usually affects the lungs in up to 67% of cases. In 2019, there were estimated to be over 10 million tuberculosis cases in the world, in the same year TB was between the ten leading causes of death, and the deadliest from a single infectious agent. Chest X-ray (CXR) has recently been promoted by the WHO as a tool possibly placed early in screening and triaging algorithms for TB detection. Numerous TB prevalence surveys have demonstrated that CXR is the most sensitive screening tool for pulmonary TB and that a significant proportion of people with TB are asymptomatic in the early stages of the disease. This study presents experimentation of classic convolutional neural network architectures on public CRX databases in order to create a tool applied to the diagnostic aid of TB in chest X-ray images. As result the study has an AUC ranging from 0.78 to 0.84, sensitivity from 0.76 to 0.86 and specificity from 0.58 to 0.74 depending on the network architecture. The observed performance by these metrics alone are within the range of metrics found in the literature, although there is much room for metrics improvement and bias avoiding. Also, the usage of the model in a triage use-case could be used to validate the efficiency of the model in the future.
has issue date	2020-05-23(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50423-6_42
has license	no-cc
sha1sum (hex)	1539b362de00fa2ed243f190e423db054327bea0
schema:url	https://doi.org/10.1007/978-3-030-50423-6_42
resource representing a document's title	Preliminary Results on Pulmonary Tuberculosis Detection in Chest X-Ray Using Convolutional Neural Networks
has PubMed Central identifier	PMC7303695
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:1539b362de00fa2ed243f190e423db054327bea0#body_text
is schema:about of	named entity 'convolutional neural network' named entity 'surveys' named entity 'disease' named entity 'metrics' named entity 'Chest X-Ray' named entity 'HAVE' named entity 'EXPERIMENTATION' named entity 'USE' named entity 'TRIAGE' named entity 'DATABASES' covid:arg/1539b362de00fa2ed243f190e423db054327bea0 named entity 'validate' named entity 'tool' named entity 'create' named entity 'mycobacterium tuberculosis complex' named entity 'This' named entity 'early stages' named entity 'detection' named entity 'sensitive' named entity 'CRX' named entity 'study' named entity 'efficiency' named entity 'mycobacterium tuberculosis complex' named entity 'diagnostic aid' named entity 'chest X-ray' named entity 'infectious agent' named entity 'CXR' named entity 'Convolutional Neural Networks' named entity 'Pulmonary Tuberculosis' named entity 'demonstrated' named entity 'US Food and Drug Administration' named entity 'CV2' named entity 'hyperthreaded' named entity 'Colab' named entity 'Jupyter' named entity 'weight decay' named entity 'Google' named entity 'Shenzhen' named entity 'chest X-ray' named entity 'triage' named entity 'FDA' named entity 'Matplotlib' named entity 'Tuberculosis' named entity 'HDF5' named entity 'HIV status' named entity 'Receiver Operating Characteristic' named entity 'chest radiography' named entity 'GoogLeNet' named entity 'drug treatment' named entity 'weight decay' named entity 'bacteria' named entity 'HDF5' named entity 'triage' named entity 'GoogLeNet' named entity 'lung disease' named entity 'class function' named entity 'TB treatment' named entity 'tuberculosis' named entity 'pulmonary tuberculosis' named entity 'CXR' named entity 'NIH' named entity 'epidemic' named entity 'optimization algorithm' named entity 'confusion matrix' named entity 'CXR' named entity 'Keras' named entity 'National Institutes of Health' named entity 'network architecture' named entity 'Spain' named entity '0.01' named entity 'respiratory complications' named entity 'virtual machine' named entity 'China' named entity 'convolutional neural network' named entity 'WHO' named entity '50 million'

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