A Novel Bagged Ensemble Approach for Accurate Histopathological Breast Cancer Classification Using Transfer Learning and Convolutional Neural Networks

This paper introduces a novel bagged ensemble approach for a binary classification using convolutional neural networks (CNNs) and transfer learning strategies. The CNN is trained independently on different bootstrapped samples (bags) of the training data, and the predictions are aggregated using majority voting to obtain the final classification result of the deep bagging ensemble. The BreakHis dataset, encompassing four magnification factors (40×, 100×, 200×, and 400×), is used for evaluation, and in addition to the four-metrics (accuracy, precision, sensitivity, and F1-score), the Scott-knot statistical test and Borda count voting method are employed to comprehensively assess the performance of the different models and rank them. Experimental results demonstrated that the proposed approach achieved high values over the four metrics for breast cancer classification. The comparative analysis highlights the ability of the deep bagging ensembles to capture diverse and complementary features from different bags, and it showcases their superiority compared to the single CNNs and the hybrid bagging ensembles, where the pre-trained deep learning models are only used for feature extraction. The findings suggested that the simplicity and effectiveness of combining fine-tuned CNNs with bagging ensembles, make the proposed approach an attractive choice for practical implementation, as it holds a promise for accurate and reliable classification of histopathological breast cancer images. Furthermore, this approach can potentially be applied to other medical image classification tasks, providing a more efficient and accurate diagnosis for a range of diseases.