Thèses en ligne de l'université 8 Mai 1945 Guelma

Combinaison de classifieurs pour la reconnaissance des anomalies mammaires

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dc.contributor.author FADEL, Ouissal
dc.date.accessioned 2023-11-22T14:00:27Z
dc.date.available 2023-11-22T14:00:27Z
dc.date.issued 2023
dc.identifier.uri http://dspace.univ-guelma.dz/jspui/handle/123456789/14978
dc.description.abstract Breast cancer is the most common cancer in women, where early detection plays an important role, increasing the chances of a complete cure and in some cases, enabling less intrusive treatment. In recent years, the integration of machine learning (ML) techniques and especially deep learning (DL) have shown promising results in breast cancer classification. The experiments we have carried out in this project combine three transfer learning models (VGG16, Resnet50 and efficient_B0) and five classical supervised learning techniques (MLP, SVM, random forests, decision trees and logistic regression) using haralick features as well as patient age and mammography density. In this work, we used four complete databases. These experiments will enable us to choose the best combination that includes efficient_B0 and SVM. This combination achieves a recognition rate of 96.70%, a precision of 99.6%, a recall of 93.8% and an F1 score of 96.6%. en_US
dc.language.iso fr en_US
dc.publisher University of Guelma en_US
dc.subject Machine Learning, Deep Learning, Transfer Learning, Mammography, Feature Extraction. en_US
dc.title Combinaison de classifieurs pour la reconnaissance des anomalies mammaires en_US
dc.type Working Paper en_US


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