Dual-View Colorized Mammogram ROIs With EfficientNet-B7 and Attention Network for Breast Lesion Classification
Thanh-Tam Nguyen, Nguyễn Thanh Hải, Thanh-Nghia Nguyen, Ba-Viet Ngo · IEEE Access · 2026
Breast cancer diagnosis using mammography can be significantly enhanced by integrating information from multi-source fusion and complementary imaging perspectives. In this paper, we propose a novel deep learning framework with dataset fusion for breast lesion classification. In particular, our approach fuses mammogram datasets of both Medio-Lateral-Oblique (MLO) and Cranio-Caudal (CC) views in four breast lesion categories from two data sources to ensure comprehensive lesion representation, and a combined EfficientNet-B7 and Multi-Head Attention (MHA) network is applied to classify these breast lesion categories. Therefore, the largest Region of Interest (ROI) in each image is extracted and then colorized before inserted into the network for lesion classification. Two colorized MLO and CC ROIs are separately processed by two parallel EfficientNet-B7 networks to produce the combined high-level features. The combined feature vectors from the dual EfficientNet-B7 network are then concatenated to yield a unified feature representation, which is subsequently refined using the MHA network to prioritize the most salient patterns and suppress background noise before classified using a fully connected head. To select an optimal classification model for these breast lesion categories, we performed four different models for two cases with and without an MHA network. Therefore, extensive experiments on integrated multi-source mammogram datasets demonstrate that the proposed framework outperforms conventional single-view and non-attentive models, achieving around 93.9% accuracy for breast lesion classification. The results highlight the value of dataset fusion and MHA-based feature selection in advancing computer-aided mammography.