Ultrasonography is one of the main imaging methods for diagnosing thyroid nodules. Automatic differentiation between benign and malignant nodules in ultrasound images can great assist inexperienced clinicians in their diagnosis. The core of problem is the effective utilization of the features of ultrasound images. In this study, we propose a method that is based on the combination of conventional ultrasound and ultrasound elasticity images based on a convolutional neural network and introduces richer feature information for the classification of benign and malignant thyroid nodules. First, the conventional network model performs pretraining on ImageNet and transfers the feature parameters to the ultrasound image domain by transfer learning so that depth features may be extracted and small samples may be processed. Then, we combine the depth features of conventional ultrasound and ultrasound elasticity images to form a hybrid feature space. Finally, the classification is completed on the hybrid feature space, and an end-to-end CNN model is implemented. The experimental results demonstrate that the accuracy of the proposed method is 0.9470, which is better than that of other single data-source methods under the same conditions.

中文翻译：

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结合常规超声和超声弹性成像诊断甲状腺良恶性结节。

超声检查是诊断甲状腺结节的主要影像学方法之一。超声图像中良性和恶性结节之间的自动区分可以极大地帮助经验不足的临床医生进行诊断。问题的核心是超声图像特征的有效利用。在这项研究中，我们提出了一种基于传统超声和基于卷积神经网络的超声弹性图像相结合的方法，并引入了更丰富的特征信息来对甲状腺的良性和恶性结节进行分类。首先，常规网络模型在ImageNet上执行预训练，并通过转移学习将特征参数转移到超声图像域，以便可以提取深度特征并可以处理小样本。然后，我们将常规超声和超声弹性图像的深度特征结合起来，形成一个混合特征空间。最后，在混合特征空间上完成分类，并实现了端到端的CNN模型。实验结果表明，在相同条件下，该方法的准确性为0.9470，优于其他单一数据源方法。