Deep Heterogeneous Convolution Neural Networks Ensembles for Pathological Breast Cancer Diagnosis (Neurips 2023 - NAMLA 2023)

This study proposes a deep end-to-end heterogeneous ensemble approach (DEHtE) for breast histopathological images classification. The ensemble approach combines two to seven learners among the following popular deep convolutional neural networks: VGG16, VGG19, ResNet50, Inception V3, Inception ResNet V2, Xception, and MobileNet V2. It is based on three selection criteria (by accuracy, by diversity, and by both accuracy and diversity) and two voting methods (majority voting and weighted voting). An experimental evaluation on the popular BreakHis dataset demonstrates a significant increase in performance compared to the learner ResNet50 used as a baseline with an accuracy rising from 78.14%, 78.57%, 82.80% and 79.43% to 93.80%, 93.40%, 93.30%, and 91.80% through the BreakHis dataset’s four magnification factors: 40X, 100X, 200X, and 400X respectively. This study proposes a deep end-to-end heterogeneous ensemble approach (DEHtE) for breast histopathological images classification. The ensemble approach combines two to seven learners among the following popular deep convolutional neural networks: VGG16, VGG19, ResNet50, Inception V3, Inception ResNet V2, Xception, and MobileNet V2. It is based on three selection criteria (by accuracy, by diversity, and by both accuracy and diversity) and two voting methods (majority voting and weighted voting). An experimental evaluation on the popular BreakHis dataset demonstrates a significant increase in performance compared to the learner ResNet50 used as a baseline with an accuracy rising from 78.14%, 78.57%, 82.80% and 79.43% to 93.80%, 93.40%, 93.30%, and 91.80% through the BreakHis dataset’s four magnification factors: 40X, 100X, 200X, and 400X respectively.