Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study
The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stra...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/178998 |
| _version_ | 1826114945559822336 |
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| author | Etehadtavakol, Mehdi Etehadtavakol, Mahnaz Ng, Eddie Yin Kwee |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Etehadtavakol, Mehdi Etehadtavakol, Mahnaz Ng, Eddie Yin Kwee |
| author_sort | Etehadtavakol, Mehdi |
| collection | NTU |
| description | The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities. |
| first_indexed | 2024-10-01T03:47:29Z |
| format | Journal Article |
| id | ntu-10356/178998 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:47:29Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1789982024-07-20T16:48:10Z Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study Etehadtavakol, Mehdi Etehadtavakol, Mahnaz Ng, Eddie Yin Kwee School of Mechanical and Aerospace Engineering Engineering Thermography Artificial intelligence The thyroid gland, a key component of the endocrine system, is pivotal in regulating bodily functions. Thermography, a non-invasive imaging technique utilizing infrared cameras, has emerged as a diagnostic tool for thyroid-related conditions, offering advantages such as early detection and risk stratification. Artificial intelligence (AI) has demonstrated success in medical diagnostics, and its integration into thermal imaging analysis holds promise for improving diagnostic capabilities. This study aims to explore the potential of AI, specifically convolutional neural networks (CNNs), in enhancing the analysis of thyroid thermograms for the detection of nodules and abnormalities. Submitted/Accepted version 2024-07-15T08:45:50Z 2024-07-15T08:45:50Z 2024 Journal Article Etehadtavakol, M., Etehadtavakol, M. & Ng, E. Y. K. (2024). Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study. Computer Methods and Programs in Biomedicine, 251, 108209-. https://dx.doi.org/10.1016/j.cmpb.2024.108209 0169-2607 https://hdl.handle.net/10356/178998 10.1016/j.cmpb.2024.108209 38723436 2-s2.0-85192300189 251 108209 en Computer Methods and Programs in Biomedicine © 2024 Elsevier B.V. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at: http://dx.doi.org/10.1016/j.cmpb.2024.108209 application/pdf |
| spellingShingle | Engineering Thermography Artificial intelligence Etehadtavakol, Mehdi Etehadtavakol, Mahnaz Ng, Eddie Yin Kwee Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study |
| title | Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study |
| title_full | Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study |
| title_fullStr | Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study |
| title_full_unstemmed | Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study |
| title_short | Enhanced thyroid nodule segmentation through U-Net and VGG16 fusion with feature engineering: a comprehensive study |
| title_sort | enhanced thyroid nodule segmentation through u net and vgg16 fusion with feature engineering a comprehensive study |
| topic | Engineering Thermography Artificial intelligence |
| url | https://hdl.handle.net/10356/178998 |
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