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The Thyroid Gland01:23

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Updated: Sep 20, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Multi-Scale Densely Connected Convolutional Neural Network for Automated Thyroid Nodule Classification.

Luoyan Wang1,2, Xiaogen Zhou1,2, Xingqing Nie1,2

  • 1College of Physics and Information Engineering, Fuzhou University, Fuzhou, China.

Frontiers in Neuroscience
|June 6, 2022
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Summary

A new deep convolutional neural network (CNN) model, n-ClsNet, accurately classifies thyroid nodules in ultrasound images. This automated approach achieved 93.8% accuracy, improving diagnostic capabilities.

Keywords:
deep convolutional neural networkdensely connectionhybrid atrous convolutionmulti-scalethe thyroid nodule classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Thyroid nodule classification from ultrasound images is crucial for accurate diagnosis.
  • Automated methods can enhance the efficiency and accuracy of thyroid nodule detection.

Purpose of the Study:

  • To introduce a novel deep convolutional neural network (CNN) model, n-ClsNet, for automated thyroid nodule classification.
  • To improve the accuracy of thyroid nodule diagnosis using advanced deep learning techniques.

Main Methods:

  • Developed n-ClsNet, a novel CNN model incorporating a multi-scale classification layer, skip blocks, and a hybrid atrous convolution (HAC) block.
  • The multi-scale classification layer and skip blocks facilitate the utilization of multi-scale image features.
  • The HAC block replaces traditional downpooling layers to preserve spatial information.

Main Results:

  • The n-ClsNet model was evaluated on the TNUI-2021 dataset for thyroid nodule classification.
  • Achieved an average accuracy (ACC) of 93.8% on the classification task.
  • Outperformed several existing state-of-the-art classification methods in terms of accuracy.

Conclusions:

  • The proposed n-ClsNet model demonstrates high efficacy in automated thyroid nodule classification.
  • n-ClsNet offers a promising advancement for improving the accuracy and efficiency of thyroid nodule diagnosis.
  • The model's architecture effectively leverages multi-scale features and spatial information for superior performance.