论文标题
超声图像的乳腺肿瘤分类的不同CNN的比较
Comparison of different CNNs for breast tumor classification from ultrasound images
论文作者
论文摘要
乳腺癌是全球最致命的癌症之一。及时检测可以降低死亡率。在临床常规中，对超声（US）成像的良性和恶性肿瘤进行分类是一项至关重要但具有挑战性的任务。因此，需要一种可以处理数据可变性的自动化方法。 在本文中，我们比较了不同的卷积神经网络（CNN）和转移学习方法，以实现自动乳腺肿瘤分类的任务。这项研究中研究的架构是VGG-16和Inception V3。研究了两种不同的培训策略：第一个是使用验证的模型作为特征提取器，第二个是要微调预训练的模型。总共使用了947张图像，587个对应于美国良性肿瘤的图像和360个具有恶性肿瘤的图像。 678张图像用于训练和验证过程，而269张图像用于测试模型。 接收器操作特征曲线（AUC）下的精度和面积用作性能指标。最好的性能是通过微调VGG-16获得的，精度为0.919，AUC为0.934。获得的结果为进一步研究的机会开放，以改善癌症检测。
Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. An automated method, which can deal with the variability of data is therefore needed. In this paper, we compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The architectures investigated in this study were VGG-16 and Inception V3. Two different training strategies were investigated: the first one was using pretrained models as feature extractors and the second one was to fine-tune the pre-trained models. A total of 947 images were used, 587 corresponded to US images of benign tumors and 360 with malignant tumors. 678 images were used for the training and validation process, while 269 images were used for testing the models. Accuracy and Area Under the receiver operating characteristic Curve (AUC) were used as performance metrics. The best performance was obtained by fine tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934. The obtained results open the opportunity to further investigation with a view of improving cancer detection.
