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Automated thyroid nodule classification in ultrasound imaging using a hybrid vision transformer and Wasserstein GAN with gradient penalty.

Scientific reports·2025

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Updated: Jan 15, 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

EvoThy-Net: an evolutionary encoder-decoder network for thyroid nodule segmentation in ultrasound imaging.

Naga Sujini Ganne¹, Sivadi Balakrishna²

¹Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhrapradesh, India.

Scientific Reports

|January 13, 2026

Summary

This study introduces EvoThy-Net, an AI method using evolutionary algorithms to automatically segment thyroid nodules in ultrasound images. It achieves superior accuracy, improving cancer diagnosis through efficient and reliable automated segmentation.

Keywords:

Block-based network Neural architecture search Thyroid nodule segmentation Ultrasound images

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Area of Science:

Medical Imaging
Artificial Intelligence
Endocrinology

Background:

Thyroid nodules are common and require accurate segmentation for cancer diagnosis.
Manual segmentation is time-consuming and prone to errors.
Automating thyroid nodule segmentation (TNS) in ultrasound images is challenging due to complex tissue structures.

Purpose of the Study:

To develop an automated method for thyroid nodule segmentation (TNS) in ultrasound images.
To optimize neural network architectures for TNS using evolutionary algorithms.
To enhance segmentation performance through attention mechanisms.

Main Methods:

An evolutionary neural architecture search (NAS) method utilizing the Improved Teaching-Learning-Based Optimization (ITLBO) algorithm.
Development of an encoder-decoder architecture with dynamic network structure optimization.
Integration of attention blocks to improve segmentation accuracy.

Main Results:

The proposed EvoThy-Net method demonstrated superior performance in thyroid nodule segmentation.
Evaluation on two public ultrasound datasets confirmed the effectiveness of the approach.
The method outperformed existing state-of-the-art models in TNS accuracy.

Conclusions:

EvoThy-Net offers an efficient and accurate automated solution for thyroid nodule segmentation.
The evolutionary NAS approach successfully optimizes network architectures for medical image analysis.
This work advances the potential of AI in improving thyroid cancer diagnosis.