|
| 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
Attentionmaps |
| Index. 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 : |
pdf |
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
Attentionmaps |
| Index. 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 : |
pdf |
|
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