Frontiers in Neuroscience (May 2022)

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

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

DOI
https://doi.org/10.3389/fnins.2022.878718
Journal volume & issue
Vol. 16

Abstract

Read online

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.

Keywords