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Updated: Mar 7, 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

Deep learning-based multi-class classification of thyroid disorders on Tc-99m scintigraphy using modified

Hafiz Muhammad Usman Ghani¹, Javed Khan², Naimat Ullah Khan³

¹Department of Physics, University of Science & Technology Bannu, Bannu, Pakistan.

|March 6, 2026

Summary

This summary is machine-generated.

This study introduces an AI system using DenseNet-201 for accurate thyroid disorder diagnosis, achieving 91.48% accuracy. The automated tool assists physicians in identifying seven thyroid conditions, improving diagnostic efficiency.

Keywords:

DenseNet-201 Thyroid scintigraphy classification diagnosis thyroid gland disorders

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Area of Science:

Medical Imaging and Diagnostics
Artificial Intelligence in Healthcare
Endocrinology

Background:

Thyroid gland dysfunction poses significant health risks, necessitating accurate and timely diagnosis.
Early detection of thyroid disorders is crucial for effective patient recovery and management.
Current diagnostic methods may benefit from enhanced accuracy and efficiency through technological integration.

Purpose of the Study:

To develop an automated system for assisting physicians in the clinical diagnosis of thyroid gland disorders.
To leverage deep learning, specifically the DenseNet-201 model, for classifying various thyroid conditions.
To enhance the accuracy and efficiency of thyroid disorder diagnosis through an AI-powered approach.

Main Methods:

Utilized the DenseNet-201 deep neural network model with transfer learning capabilities.
Modified the fully connected and classification layers of DenseNet-201 for specific thyroid condition classification.
Trained and evaluated the model on seven categories: cold nodule, hot nodule, multi-nodular goiter, nodular goiter, thyroiditis, toxic diffuse goiter, and normal thyroid conditions.

Main Results:

Achieved high performance metrics, including 91.48% accuracy, 98.58% specificity, 91.57% precision, 91.48% sensitivity, and a 0.988 Area Under the Curve (AUC).
The kappa coefficient, measuring agreement with expert diagnosis, was 0.9148, indicating strong concordance.
Demonstrated superior performance compared to contemporary methods in key diagnostic metrics.

Conclusions:

The developed automated system shows significant potential for clinical application in thyroid disorder diagnosis.
The AI model effectively assists physicians by providing accurate classification of various thyroid conditions.
The high accuracy and strong agreement with expert diagnoses suggest the system can improve patient care and outcomes.