Deep Hybrid Bagging Ensembles for Classifying Histopathological Breast Cancer Images

Abstract

This paper proposes the use of transfer learning and ensemble learning for binary classification of breast cancer histological images over the four magnification factors of the BreakHis dataset: 40×, 100×, 200× and 400×. The proposed bagging ensembles are implemented using a set of hybrid architectures that combine pre-trained deep learning techniques for feature extraction with machine learning classifiers as base learners (MLP, SVM and KNN). The study evaluated and compared: (1) bagging ensembles with their base learners, (2) bagging ensembles with a different number of base learners (3, 5, 7 and 9), (3) single classifiers with the best bagging ensembles, and (4) best bagging ensembles of each feature extractor and magnification factor. The best cluster of the outperforming models was chosen using the Scott Knott (SK) statistical test, and the top models were ranked using the Borda Count voting system. The best bagging ensemble achieved a mean accuracy value of 93.98%, and was constructed using 3 base learners, 200× as a magnification factor, MLP as a classifier, and DenseNet201 as a feature extractor. The results demonstrated that bagging hybrid deep learning is an effective and a promising approach for the automatic classification of histopathological breast cancer images.