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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

Thyroid nodule recognition based on feature selection and pixel classification methods.

Dorin Bibicu¹, Luminita Moraru, Anjan Biswas

¹Faculty of Sciences and Environment, Physics Department, Dunarea de Jos University of Galati, Galati, Romania.

Journal of Digital Imaging

|May 2, 2012

Summary

This summary is machine-generated.

This study introduces a statistical texture analysis method for identifying thyroid nodules in ultrasound images. The automated classifier achieved 83% accuracy on whole images and 91% on regions of interest, aiding in thyroid nodule detection.

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Area of Science:

Medical Imaging
Biomedical Engineering
Computer-Aided Diagnosis

Background:

Thyroid nodules require accurate identification for diagnosis and treatment.
Automated analysis of ultrasound images can improve diagnostic efficiency and consistency.
Texture analysis offers a quantitative approach to characterizing tissue variations.

Purpose of the Study:

To design and implement a classifier framework for automatic thyroid nodule identification from ultrasound images.
To develop a discriminative texture analysis method based on statistical features.
To correlate texture parameters with specific biological areas within thyroid ultrasound images.

Main Methods:

Extraction of discriminative first-order statistical texture features.
Development of a classifier for automatic feature optimization and selection.
Pixel classification and location analysis to correlate texture parameters with biological areas.
Validation using Receiver Operating Characteristic (ROC) curves.

Main Results:

The proposed texture analysis method successfully identified thyroid nodules.
Achieved a correct classification rate of 83% when analyzing whole ultrasound images.
Achieved a higher correct classification rate of 91% when analyzing regions of interest (ROIs).

Conclusions:

Statistical texture analysis provides a robust method for characterizing thyroid tissue.
The developed classifier framework effectively identifies thyroid nodules in ultrasound images.
The approach demonstrates significant potential for computer-aided diagnosis of thyroid abnormalities.