Table_1_A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification.XLSX

Automated thyroid nodule classification in ultrasound images is an important way to detect thyroid nodules and to make a more accurate diagnosis. In this paper, we propose a novel deep convolutional neural network (CNN) model, called n-ClsNet, for thyroid nodule classification. Our model consists of a multi-scale classification layer, multiple skip blocks, and a hybrid atrous convolution (HAC) block. The multi-scale classification layer first obtains multi-scale feature maps in order to make full use of image features. After that, each skip-block propagates information at different scales to learn multi-scale features for image classification. Finally, the HAC block is used to replace the downpooling layer so that the spatial information can be fully learned. We have evaluated our n-ClsNet model on the TNUI-2021 dataset. The proposed n-ClsNet achieves an average accuracy (ACC) score of 93.8% in the thyroid nodule classification task, which outperforms several representative state-of-the-art classification methods.

超声图像中的甲状腺结节自动化分类，是检出甲状腺结节并实现精准诊断的重要手段。本文提出一种用于甲状腺结节分类的新型深度卷积神经网络（Convolutional Neural Network，CNN）模型，命名为n-ClsNet。本模型包含多尺度分类层、多个跳跃块以及混合空洞卷积（HAC）模块。该多尺度分类层首先提取多尺度特征图，以充分利用图像特征信息；随后，每个跳跃块通过在不同尺度下传递信息，学习适用于图像分类的多尺度特征；最终，本研究采用HAC模块替代下采样层，以实现对空间信息的充分学习。本研究在TNUI-2021数据集上对n-ClsNet模型开展了性能评估。所提出的n-ClsNet在甲状腺结节分类任务中取得了93.8%的平均准确率（ACC），其性能优于若干具有代表性的当前顶尖分类方法。