Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN

TL;DR

This paper introduces a CNN ensemble model using bagging and stacking techniques for breast cancer classification, achieving high accuracy and outperforming standard models on the DDSM dataset.

Contribution

The study presents a novel ensemble CNN approach combining bagging and stacking, significantly improving classification accuracy over existing models.

Findings

Achieved 98.84% accuracy for binary classification.

Outperformed VGG16 and ResNet-50 by over 6% in accuracy.

Demonstrated robustness with high recall and F1 scores.

Abstract

This paper proposes a CNN classification network based on Bagging and stacking ensemble learning methods for breast cancer classification. The model was trained and tested on the public dataset of DDSM. The model is capable of fast and accurate classification of input images. According to our research results, for binary classification (presence or absence of breast cancer), the accuracy reached 98.84%, and for five-class classification, the accuracy reached 98.34%. The model also achieved a micro-average recall rate of 94.80% and an F1 score of 94.19%. In comparative experiments, we compared the effects of different values of bagging_ratio and n_models on the model, as well as several methods for ensemble bagging models. Furthermore, under the same parameter settings, our BSECNN outperformed VGG16 and ResNet-50 in terms of accuracy by 8.22% and 6.33% respectively.