AUTHOR=Wang Luoyan , Zhou Xiaogen , Nie Xingqing , Lin Xingtao , Li Jing , Zheng Haonan , Xue Ensheng , Chen Shun , Chen Cong , Du Min , Tong Tong , Gao Qinquan , Zheng Meijuan 

TITLE=A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification

JOURNAL=Frontiers in Neuroscience

VOLUME=16

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.878718

DOI=10.3389/fnins.2022.878718

ISSN=1662-453X

ABSTRACT=<p>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.</p>