Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: A comprehensive study

Mehdi Etehadtavakol ¹, Mahnaz Etehadtavakol ², Eddie Y K Ng ³

⁰School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, 1985717443, Iran; Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Computer Methods and Programs in Biomedicine +

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May 9, 2024

View abstract on PubMed

Summary

Artificial intelligence (AI) enhances thyroid nodule detection using thermal imaging. This study integrates AI with feature engineering for accurate segmentation, improving diagnostic capabilities for thyroid conditions.

Area Of Science

Endocrinology and Medical Imaging
Artificial Intelligence in Healthcare

Background

Thermography offers non-invasive thyroid imaging for early detection and risk stratification.
Artificial intelligence (AI) shows promise in advancing medical diagnostics, particularly in thermal imaging analysis.
Thyroid gland dysfunction impacts numerous bodily functions, necessitating improved diagnostic tools.

Purpose Of The Study

To explore the potential of AI, specifically convolutional neural networks (CNNs), in analyzing thyroid thermograms.
To enhance the detection of thyroid nodules and abnormalities using AI-driven thermal image analysis.
To investigate AI's role in improving diagnostic accuracy for thyroid conditions.

Main Methods

Integration of AI and machine learning techniques for enhanced thyroid thermal image analysis.
Proposed fusion of U-Net and VGG16 models combined with feature engineering (FE) for thyroid nodule segmentation.
Leveraging feature engineering in transfer learning for nodule segmentation, even with limited datasets.

Main Results

Demonstrated efficacy of the AI approach across four studies, even with limited data.
Significant improvement in dice coefficient observed with feature engineering (FE) in study 4.
Incorporating radiomics with FE yielded substantial improvements in segmentation accuracy for small masked regions.

Conclusions

AI demonstrates significant potential for precise and efficient thyroid nodule segmentation.
The proposed AI methods pave the way for enhanced thyroid health assessment.
Further refinement of AI models can lead to even higher diagnostic accuracy.

Keywords:

Artificial Intelligence Segmentation Thermography Thyroid Nodule