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Updated: May 19, 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

Dual-discriminator network-based classification method for breast ultrasound imaging.

Xue Zhao¹, Huanyu Zhao¹, Zhiying Cheng¹

¹Department of Medical Imaging, Chifeng Municipal Hospital, Chifeng, China.

Quantitative Imaging in Medicine and Surgery

|May 18, 2026

Summary

This summary is machine-generated.

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This study introduces a novel Dual-Discriminator Generative Adversarial Network (GAN) to improve breast cancer detection in ultrasound images. The method enhances accuracy and interpretability, crucial for early diagnosis and effective treatment.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer poses a significant global health challenge, necessitating early detection through ultrasound imaging.
Challenges in medical image classification include small datasets, data imbalance, and the need for robust feature extraction and interpretability.
Transfer learning from natural images is common but faces limitations with medical data.

Purpose of the Study:

To address data imbalance in breast ultrasound image classification.
To enhance the accuracy and interpretability of breast cancer diagnosis using AI.
To improve early detection and patient outcomes for breast cancer.

Main Methods:

Developed a novel Dual-Discriminator Generative Adversarial Network (GAN) for iterative data synthesis on unbalanced datasets.

Keywords:

Breast cancer data synthesis deep learning transfer learning ultrasound imaging

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Last Updated: May 19, 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

Incorporated channel and spatial attention mechanisms for intricate feature recognition in classification.

Validated the approach on the Breast Ultrasound Images Dataset (BUSI) and a self-constructed dataset.

Main Results:

Achieved a top accuracy of 96.0% on the BUSI dataset.
Attained 95.8% accuracy on the self-constructed evaluation dataset.
Demonstrated competitive performance against state-of-the-art methods.

Conclusions:

The proposed method effectively addresses data imbalance and enhances classification accuracy.
Attention mechanisms improve the recognition of critical diagnostic features in ultrasound images.
Visualization methods confirm the practical diagnostic value for breast cancer identification.