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Titre : Biomedical Data Analysis by Deep Architectures Type de document : texte imprimé Auteurs : Tcheir ,Abir, Auteur ; Abdelouahab Moussaoui, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (59 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : OVID-19
chestX-ray
DeepLearning
AttentionmapsIndex. décimale : 004 - Informatique Résumé :
OVID-19causeslunginflammationandlesions,andchestX-rayimagesareremarkably
suitable fordifferentiatingthenewdiseasefrompatientswithotherlungdiseases.
In thispaper,weproposeacomputermodeltoclassifyX-rayimagesofpatientsdiagnosed
with COVID-19.Thedatasetsutilizedinthisexperimentaretwo.Firstly,adatasetof9545X-ray
images including4045imageswithconfirmedCovid-19disease,and5500imagesofNonCovid-19.
Secondly,adatasetof13677X-rayimagesincluding3424imageswithconfirmedCovid-19disease,
1345 imageswithconfirmedviralpneumonia,and8908imagesofnormalconditions.Theresults
suggest thatDeepLearningwithX-rayimagingmayextractsignificantbiomarkersrelated
to theCovid-19disease,Thisworkhasconsideredthewellknownpre-trainedarchitectures,
suchasEfficientNetB0,DenseNet121,Vgg16,ResNet50,InceptionV3andMobileNetV2forthe
experimental evaluation.
The performanceoftheconsideredarchitecturesisevaluatedbycomputingthecommonper-
formance measures.TheresultoftheexperimentalevaluationconfirmsthattheEfficientNetB0
pre-trained transferlearning-basedmodelofferedbetterclassificationaccuracy(98.40%)onthe
considered imagedataset1classificationand(97.20%)ontheconsideredimagedataset2,were
also generatedAttentionmapsforprediction,whichrepresentsakeyexplanatorystepaimedat
increasing confidenceinthefinaldecision.Côte titre : MAI/0528 En ligne : https://drive.google.com/file/d/1pF3-IE1TmVOr85fp2nF7Z0Wrn9T3QRSk/view?usp=shari [...] Format de la ressource électronique : Biomedical Data Analysis by Deep Architectures [texte imprimé] / Tcheir ,Abir, Auteur ; Abdelouahab Moussaoui, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (59 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : OVID-19
chestX-ray
DeepLearning
AttentionmapsIndex. décimale : 004 - Informatique Résumé :
OVID-19causeslunginflammationandlesions,andchestX-rayimagesareremarkably
suitable fordifferentiatingthenewdiseasefrompatientswithotherlungdiseases.
In thispaper,weproposeacomputermodeltoclassifyX-rayimagesofpatientsdiagnosed
with COVID-19.Thedatasetsutilizedinthisexperimentaretwo.Firstly,adatasetof9545X-ray
images including4045imageswithconfirmedCovid-19disease,and5500imagesofNonCovid-19.
Secondly,adatasetof13677X-rayimagesincluding3424imageswithconfirmedCovid-19disease,
1345 imageswithconfirmedviralpneumonia,and8908imagesofnormalconditions.Theresults
suggest thatDeepLearningwithX-rayimagingmayextractsignificantbiomarkersrelated
to theCovid-19disease,Thisworkhasconsideredthewellknownpre-trainedarchitectures,
suchasEfficientNetB0,DenseNet121,Vgg16,ResNet50,InceptionV3andMobileNetV2forthe
experimental evaluation.
The performanceoftheconsideredarchitecturesisevaluatedbycomputingthecommonper-
formance measures.TheresultoftheexperimentalevaluationconfirmsthattheEfficientNetB0
pre-trained transferlearning-basedmodelofferedbetterclassificationaccuracy(98.40%)onthe
considered imagedataset1classificationand(97.20%)ontheconsideredimagedataset2,were
also generatedAttentionmapsforprediction,whichrepresentsakeyexplanatorystepaimedat
increasing confidenceinthefinaldecision.Côte titre : MAI/0528 En ligne : https://drive.google.com/file/d/1pF3-IE1TmVOr85fp2nF7Z0Wrn9T3QRSk/view?usp=shari [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0528 MAI/0528 Mémoire Bibliothéque des sciences Français Disponible
Disponible