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

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...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9160335/
https://www.ncbi.nlm.nih.gov/pubmed/35663553
http://dx.doi.org/10.3389/fnins.2022.878718
Descripción
Sumario: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.