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Updated: May 12, 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

Support Vector Machine based diagnostic system for thyroid cancer using statistical texture features.

B Gopinath¹, N Shanthi

¹Department of Electronics and Communication Engineering, Info Institute of Engineering, Coimbatore, India. gopiphd@yahoo.com

Asian Pacific Journal of Cancer Prevention : APJCP

|March 29, 2013

Summary

This summary is machine-generated.

This study developed an automated system for diagnosing thyroid cancer from fine needle aspiration cytology (FNAC) images. The system achieved 96.7% accuracy using statistical texture analysis and a Support Vector Machine (SVM) classifier.

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Area of Science:

Medical Imaging
Computational Pathology
Oncology

Background:

Accurate diagnosis of thyroid cancer from fine needle aspiration cytology (FNAC) is crucial for patient management.
Automated diagnostic systems can improve efficiency and consistency in pathological assessments.

Purpose of the Study:

To develop an automated computer-aided diagnostic system for thyroid cancer detection in FNAC images.
To achieve high sensitivity and specificity in classifying benign versus malignant thyroid nodules.

Main Methods:

Utilized a dataset of 110 FNAC images (40 benign/40 malignant training, 10 benign/20 malignant testing).
Employed region-based morphology segmentation for region of interest (ROI) extraction.
Extracted statistical texture features using a Gabor filter bank.
Applied a Support Vector Machine (SVM) classifier for automated diagnosis.

Main Results:

The automated system achieved a diagnostic accuracy of 96.7%.
Sensitivity reached 95% and specificity reached 100% at specific Gabor filter parameters (wavelength 4, angle 45).

Conclusions:

Statistical texture information derived from Gabor filters, combined with SVM, effectively diagnoses thyroid cancer in FNAC images.
The developed system demonstrates potential for reliable automated thyroid cancer detection.