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The Thyroid Gland

The Thyroid Gland

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: Jan 3, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains.

Dat Tien Nguyen¹, Tuyen Danh Pham¹, Ganbayar Batchuluun¹

¹Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.

Journal of Clinical Medicine

|November 20, 2019

Summary

This summary is machine-generated.

This study introduces an AI-driven method to improve thyroid cancer diagnosis from ultrasound images. The novel approach enhances classification accuracy for thyroid nodules, aiding doctors in distinguishing benign from malignant cases.

Keywords:

Fast Fourier transform artificial intelligence deep learning frequency domain spatial domain thyroid nodule classification

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Computer-aided diagnosis (CAD) systems for thyroid cancer rely on accurate image analysis.
Current CAD systems for thyroid nodule classification show limitations in performance and accuracy.
Enhancing classification accuracy is crucial for reliable computer-aided diagnosis of thyroid cancer.

Purpose of the Study:

To propose an artificial intelligence-based method for improving thyroid nodule classification accuracy.
To enhance the performance of computer-aided diagnosis systems for thyroid cancer.
To develop a robust method for classifying thyroid nodules as benign or malignant.

Main Methods:

Extraction of image features from ultrasound thyroid images in both spatial (deep learning) and frequency (Fast Fourier Transform) domains.
Implementation of a cascade classifier scheme utilizing the extracted multi-domain features.
Validation using the public Thyroid Digital Image Database (TDID) dataset.

Main Results:

The proposed artificial intelligence method significantly improved classification accuracy for thyroid nodules.
The system demonstrated superior performance compared to existing state-of-the-art methods.
Achieved up-to-date classification results on the TDID dataset for thyroid nodule diagnosis.

Conclusions:

The proposed AI-based method effectively enhances thyroid nodule classification accuracy in ultrasound images.
This approach offers a promising advancement for computer-aided diagnosis of thyroid cancer.
The combination of spatial and frequency domain features leads to superior diagnostic performance.