About: GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm

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About: GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm 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	GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
Creator	Aboul Ella, Hassan Ell Hassanien, Aboul Ezzat, Dalia
Source	ArXiv
abstract	In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121 and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is adapted to determine the best values for the hyperparameters of the DenseNet121 architecture, and to achieve a high level of accuracy in diagnosing COVID-19 disease through chest x-ray image analysis. The obtained results showed that the proposed approach was able to correctly classify 98% of the test set. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121, it was compared to another optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19 and its ability to better diagnose COVID-19 disease than the SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. As well as, the proposed approach was compared to an approach based on a CNN architecture called Inception-v3 and the manual search method for determining the values of the hyperparameters. The results of the comparison showed that the GSA-DenseNet121 was able to beat the other approach, as the second was able to classify only 95% of the test set samples.
has issue date	2020-04-09(xsd:dateTime)
has license	arxiv
sha1sum (hex)	1932ea139d08d1256a77ccdeeae510f6120ce6bf
resource representing a document's title	GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
resource representing a document's body	covid:1932ea139d08d1256a77ccdeeae510f6120ce6bf#body_text
is schema:about of	named entity 'based' named entity 'called' named entity 'architecture' named entity 'CNN' named entity 'COVID-19' named entity 'SETTING' named entity 'OBTAINED' named entity 'IMAGE ANALYSIS' named entity 'DISEASE' named entity 'GSA' named entity 'TO TEST' named entity 'BETTER' named entity 'VALUES' named entity 'PAPER' named entity 'TEST' named entity 'SEARCH ALGORITHM' named entity 'DIAGNOSE' named entity 'SKI' named entity 'COVID-19' named entity 'OPTIMUM' named entity 'DRIVER' named entity 'disease' named entity 'architecture' named entity 'test set' named entity 'search algorithm' named entity 'called' named entity 'SSD' named entity 'efficacy' named entity 'architecture' named entity 'demonstrated' named entity 'Hybrid' named entity 'Diagnosis' named entity 'optimization algorithm' named entity 'test set' named entity 'optimization algorithm' named entity 'CNN' named entity 'CNN' named entity 'search algorithm' named entity 'deep structure' named entity 'activation function' named entity 'reverse transcriptase polymerase chain reaction' named entity 'precision, recall' named entity 'false negatives' named entity 'machine translation' named entity 'Transfer learning' named entity 'optimization problem' named entity 'Chest X-ray' named entity 'MERS-CoV' named entity 'neurons' named entity 'loss rate' named entity 'grows exponentially' named entity 'ARDS' named entity 'causative agent' named entity 'SARS' named entity 'CT images' named entity 'F1 score' named entity 'Feature extraction' named entity 'loss rate' named entity 'validation set' named entity 'artificial neural networks' named entity 'validation set' named entity 'activation function' named entity 'complex networks' named entity 'severe acute respiratory syndrome coronavirus 2' named entity 'COVID' named entity 'Chest X-ray' named entity 'training set' named entity 'medical health' named entity 'convolutional layer' named entity 'inertial mass' named entity 'X-ray images' named entity 'training set' named entity 'COVID' named entity 'medical health' named entity 'radiological imaging' named entity 'respiratory disease' named entity 'CNN' named entity 'Keras' named entity 'learning rate' named entity 'MERS' named entity 'gravitational mass' named entity 'receptive field' named entity 'COVID19' named entity 'image classification' named entity 'Python'

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