HBMD-Net: Feature Fusion Based Breast Cancer Classification with Class Imbalance Resolution

Breast cancer, a widespread global disease, represents a significant threat to women's health and lives, ranking as one of the most vulnerable malignant tumors they face. Many researchers have proposed their computer-aided diagnosis systems for classifying breast cancer. The majority of these approaches primarily utilize deep learning (DL) methods, which are not entirely reliable. These approaches overlook the crucial necessity of incorporating both local and global information for precise tumor detection, despite the fact that the subtle nuances are crucial for precise breast cancer classification. In addition, there are a limited number of publicly available breast cancer datasets, and the ones that are available tend to be imbalanced in nature. Therefore, this paper presents the hybrid breast mass detection-network (HBMD-Net) to address two critical challenges: class imbalance and the need to recognize that relying solely on either global or local features falls short in achieving precise tumor classification. To overcome the problem of class imbalance, HBMD-Net incorporates the borderline synthetic minority over-sampling technique (BSMOTE). Simultaneously, it employs a feature fusion approach, combining features by utilizing ResNet50 to extract deep features that provide global information, while handcrafted features are derived using histogram orientation gradient (HOG), that provide local information. In addition, an ROI segmentation has been implemented to avoid misclassifications. This integrated strategy substantially enhances breast cancer classification performance. Moreover, the proposed method integrates the block matching and 3D (BM3D) denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The evaluation of the proposed HBMD-Net encompasses two breast ultrasound (BUS) datasets, namely BUSI and UDIAT. The proposed model has demonstrated a satisfactory performance, achieving accuracies of 99.14% and 94.49% respectively.