A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification - Wang, Luoyan; Zhou, Xiaogen; Nie, Xingqing; Lin, Xingtao; Li, Jing; Zheng, Haonan; Xue, Ensheng; Chen, Shun; Chen, Cong; Du, Min; Tong, Tong; Gao, Qinquan; et al. (Frontiers in Neuroscience, 2022)
Automated thyroid nodule classification in ultrasound images is an important way to detect thyroid nodules and to make more accurate diagnosis. In this paper, we propose a novel deep convolutional neural network 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 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 hybrid atrous convolution block is used to replace the downpooling layer so that the spatial information can be fully learnt. We have evaluated our n-ClsNet model on the TNUI-2021 dataset. The proposed n-ClsNet achieves an average ACC score of 93.8% in the thyroid nodule classification task, which outperforms several representative state-of-the-art classification methods.