Résumé:
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%.
