University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Novel Deep Learning Architecture for Predicting Heart Diseases based Transformers and Attention Mechanism with Explainability Model / Raedin Khaled Sakhri
Titre : Novel Deep Learning Architecture for Predicting Heart Diseases based Transformers and Attention Mechanism with Explainability Model Type de document : texte imprimé Auteurs : Raedin Khaled Sakhri, Auteur ; Loutfi Boufeligha, Auteur ; Moussaou,iAbdelouahab, Directeur de thèse Année de publication : 2022 Importance : 1 vol (57 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Deep Learning
Medical ImagingIndex. décimale : 004 Informatique Résumé :
Coronary artery disease is a prevalent heart disorder. The primary blood vessels
that supply the heart (coronary arteries) struggle to supply the cardiac muscle with
sufficient blood, oxygen, and nutrients. Coronary artery disease is typically caused
by cholesterol deposits (plaques) in the heart’s arteries and inflammation.
In this dissertation, we intend to propose a novel deep learning classification model
based on Transformers that will be able to classify and identify Coronary Artery
Disease in its early stages with a high degree of accuracy that outperforms current
approaches.
Here, we present three architectures, two of which are fine-tuned versions of pretrained
models trained on a large dataset, such as Vision Transformers and Swin-
Transformers, and the third being a CNN with class activation visualization (Grad-
CAM). We were able to achieve a precision of up to 98.27 percent.Côte titre : MAI/0680 En ligne : https://drive.google.com/file/d/1C2VJRsSDhK8JgFab_TJvKtUeMf0y2_0N/view?usp=share [...] Format de la ressource électronique : Novel Deep Learning Architecture for Predicting Heart Diseases based Transformers and Attention Mechanism with Explainability Model [texte imprimé] / Raedin Khaled Sakhri, Auteur ; Loutfi Boufeligha, Auteur ; Moussaou,iAbdelouahab, Directeur de thèse . - 2022 . - 1 vol (57 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Deep Learning
Medical ImagingIndex. décimale : 004 Informatique Résumé :
Coronary artery disease is a prevalent heart disorder. The primary blood vessels
that supply the heart (coronary arteries) struggle to supply the cardiac muscle with
sufficient blood, oxygen, and nutrients. Coronary artery disease is typically caused
by cholesterol deposits (plaques) in the heart’s arteries and inflammation.
In this dissertation, we intend to propose a novel deep learning classification model
based on Transformers that will be able to classify and identify Coronary Artery
Disease in its early stages with a high degree of accuracy that outperforms current
approaches.
Here, we present three architectures, two of which are fine-tuned versions of pretrained
models trained on a large dataset, such as Vision Transformers and Swin-
Transformers, and the third being a CNN with class activation visualization (Grad-
CAM). We were able to achieve a precision of up to 98.27 percent.Côte titre : MAI/0680 En ligne : https://drive.google.com/file/d/1C2VJRsSDhK8JgFab_TJvKtUeMf0y2_0N/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0680 MAI/0680 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible