Computer Science and Engineering, NIT Silchar, Silchar, 788010, Assam, India.
J Imaging Inform Med. 2024 Aug;37(4):1440-1457. doi: 10.1007/s10278-024-01046-5. Epub 2024 Feb 26.
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.
乳腺癌是一种全球性的疾病，对女性的健康和生命构成了重大威胁，是女性面临的最脆弱的恶性肿瘤之一。许多研究人员已经提出了他们的计算机辅助诊断系统来对乳腺癌进行分类。这些方法大多采用深度学习（DL）方法，但并不完全可靠。这些方法忽略了将局部和全局信息结合起来进行精确肿瘤检测的关键必要性，尽管细微差别对于精确的乳腺癌分类至关重要。此外，可用的公开乳腺癌数据集数量有限，而且现有的数据集往往存在不平衡的情况。因此，本文提出了混合乳腺肿块检测网络（HBMD-Net）来解决两个关键挑战：类不平衡和认识到仅依赖全局或局部特征不足以实现精确的肿瘤分类。为了解决类不平衡问题，HBMD-Net 采用了边界合成少数过采样技术（BSMOTE）。同时，它采用了特征融合方法，使用 ResNet50 提取全局信息的特征，并结合使用直方图方向梯度（HOG）的手工制作特征，提取局部信息。此外，还实现了 ROI 分割以避免误分类。这种集成策略大大提高了乳腺癌的分类性能。此外，该方法集成了块匹配和 3D（BM3D）去噪滤波器，有效地消除了增强系统性能的乘性噪声。对所提出的 HBMD-Net 的评估包括两个乳腺超声（BUS）数据集，即 BUSI 和 UDIAT。所提出的模型表现出令人满意的性能，分别达到了 99.14%和 94.49%的准确率。
