Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Department of Medical Imaging and Radiological Technology, Yuanpei University of Medical Technology, Hsinchu, Taiwan.
Ultrason Imaging. 2023 Mar;45(2):74-84. doi: 10.1177/01617346231162906. Epub 2023 Mar 23.
Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose breast cancer. The accurate breast segmentation and diagnosis as benign or malignant is still a challenging task in the ultrasound image. In this paper, we proposed a classification model as short-ResNet with DC-UNet to solve the segmentation and diagnosis challenge to find the tumor and classify benign or malignant with breast ultrasonic images. The proposed model has a dice coefficient of 83% for segmentation and achieves an accuracy of 90% for classification with breast tumors. In the experiment, we have compared with segmentation task and classification result in different datasets to prove that the proposed model is more general and demonstrates better results. The deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.
乳腺癌是最常见的癌症类型，仍然是世界上女性死亡的第二大主要原因。早期发现和治疗乳腺癌可以降低死亡率。乳房超声检查通常用于检测和诊断乳腺癌。准确的乳房分割和诊断为良性或恶性仍然是超声图像中的一个具有挑战性的任务。在本文中，我们提出了一种分类模型，即短 ResNet 与 DC-UNet 相结合，以解决分割和诊断挑战，找到肿瘤并对乳房超声图像进行良性或恶性分类。所提出的模型在分割任务中具有 83%的骰子系数，并在乳房肿瘤的分类中达到 90%的准确率。在实验中，我们比较了不同数据集的分割任务和分类结果，以证明所提出的模型更具通用性，并展示了更好的结果。使用短 ResNet 对肿瘤进行分类的深度学习模型，判断是良性还是恶性，结合分割任务的 DC-UNet 来辅助提高分类结果。
