About: A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan

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About: A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan 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
title	A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan
Creator	Maghsoudi, · Saeedi, Abdolkarim Saeedi, ·maryam
source	ArXiv
abstract	The corona virus is already spread around the world in many countries, and it has taken many lives. Furthermore, the world health organization (WHO) has announced that COVID-19 has reached the global epidemic stage. Early and reliable diagnosis using chest CT-scan can assist medical specialists in vital circumstances. In this work, we introduce a computer aided diagnosis (CAD) web service to detect COVID- 19 online. One of the largest public chest CT-scan databases, containing 746 participants was used in this experiment. A number of well-known deep neural network architectures consisting of ResNet, Inception and MobileNet were inspected to find the most efficient model for the hybrid system. A combination of the Densely connected convolutional network (DenseNet) in order to reduce image dimensions and Nu-SVM as an anti-overfitting bottleneck was chosen to distinguish between COVID-19 and healthy controls. The proposed methodology achieved 90.80% recall, 89.76% precision and 90.61% accuracy. The method also yields an AUC of 95.05%. Ultimately a flask web service is made public through ngrok using the trained models to provide a RESTful COVID-19 detector, which takes only 39 milliseconds to process one image. The source code is also available at https://github.com/KiLJ4EdeN/COVID_WEB. Based on the findings, it can be inferred that it is feasible to use the proposed technique as an automated tool for diagnosis of COVID-19.
has issue date	2020-06-24(xsd:dateTime)
has license	arxiv
sha1sum (hex)	34e88ab27fb8fb2b782d3b581aa36dc69dc08e55
resource representing a document's title	A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan
resource representing a document's body	covid:34e88ab27fb8fb2b782d3b581aa36dc69dc08e55#body_text
is schema:about of	named entity 'MEDICAL SPECIALISTS' named entity 'WEB SERVICE' named entity 'DIAGNOSIS' named entity 'CAD' named entity 'COVID-19' named entity 'deep neural network' named entity 'medical specialists' named entity 'Tool' named entity 'Tensorflow' named entity 'deep learning' named entity 'overfitting' named entity 'web service' named entity 'COVID' named entity 'chest X-ray' named entity 'ResNet' named entity 'neural network' named entity 'difficulty breathing' named entity 'COVID' named entity 'Residual Network' named entity 'ResNet' named entity 'source code' named entity 'Scikit-learn' named entity 'COVID' named entity 'cross validation' named entity 'test kits' named entity 'COVID' named entity 'coronavirus' named entity 'linear transformation' named entity 'Deep learning algorithms' named entity 'medical professionals' named entity 'January 30' named entity 'COVID' named entity 'influenza-A' named entity 'Vapnik' named entity 'Human coronavirus' named entity 'COVID-19' named entity 'COVID' named entity 'COVID' named entity 'April 2020' named entity 'transfer learning' named entity 'CT-scan' named entity 'linearly separable' named entity 'convolution' named entity 'coughing' named entity 'COVID-19' named entity 'decision boundary' named entity 'neural network' named entity 'training set' named entity 'CT image' named entity 'World Health Organization' named entity 'cross validation' named entity 'Bayesian Optimization' named entity 'nasopharyngeal swab' named entity 'Fever' named entity 'deep learning' named entity 'hyperplane' named entity 'COVID-19' named entity 'hyperplane' named entity 'convolution' named entity 'DenseNets' named entity 'performance metric' named entity 'neural networks' named entity 'China' named entity 'data augmentation' named entity 'CT-scan' named entity 'batch normalization' named entity 'cough' named entity 'WHO' named entity 'CNN' named entity 'kernel function' named entity 'vector' named entity 'reverse transcription polymerase chain reaction' named entity 'test set' named entity 'deep learning' named entity 'COVID' named entity 'block diagram' named entity 'COVID' named entity '2019-20 coronavirus outbreak' named entity 'Wuhan' named entity 'deep learning' named entity 'test set' named entity 'database' named entity 'objective function' named entity 'identity mapping' named entity 'rRT-PCR'

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