Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult.Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network.Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively.Conclusions:By combining the U-Net’s powerful segmentation capabilities with the capsule network’s high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings.
背景/目的：乳腺癌仍是全球女性面临的重大健康挑战，早期检测对挽救生命至关重要。尽管超声成像常用于肿瘤检测，但图像质量常存在不足，导致传统U-Net模型难以捕捉精准检测所需的细微特征，从而降低诊断准确性，增加早期精准诊断的难度。 方法：本研究提出一种融合胶囊网络的增强型U-Net模型（简称UCapsNet），以突破传统技术的局限。该模型通过增加滤波器数量与跳跃连接优化分割效果，同时利用胶囊网络的动态路由机制保持空间层次结构以提升分类性能。UCapsNet采用两阶段处理流程：首先通过增强U-Net分割肿瘤区域，随后利用胶囊网络对分割图像进行分类。 结果：我们在乳腺超声图像（BUSI）数据集上训练模型，并与VGG-19、DenseNet、MobileNet、ResNet-50及Xception等经典预训练模型进行对比测试。通过针对性改进既有研究的不足，本模型取得了优异性能：精确率、召回率及准确率分别达到98.12%、99.52%和99.22%。 结论：UCapsNet通过融合U-Net的强大分割能力与胶囊网络的高精度分类优势，显著提升了诊断精确度，有效弥补了现有方法的缺陷。研究结果表明，该模型不仅具有更优的肿瘤检测效能，在临床实际应用中也展现出更高的可靠性。
肿瘤(癌症)患者之家