Ultrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was \(81.06\%\). Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

超声图像检查是诊断甲状腺乳头状癌的首选。但是，甲状腺乳头状癌的超声图像存在清晰度，组织重叠，分辨率低等问题，难以诊断。胶囊网络（CapsNet）可以有效解决组织重叠和其他问题。本文研究了一种基于胶囊网络的新网络模型，称为ResCaps网络。ResCaps网络使用残差模块并增强了模型的抽象表达。实验结果表明，ResCaps3网络模型对甲状腺乳头状癌自制数据集的特征分类精度为\（81.06 \％\）。此外，还对Fashion-MNIST数据集进行了测试，以显示ResCaps网络模型的可靠性和有效性。值得注意的是，ResCaps网络模型不仅大大提高了CapsNet的准确性，而且为甲状腺乳头状癌超声图像病变特征的分类提供了一种有效的方法。