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Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains
Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are pr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912332/ https://www.ncbi.nlm.nih.gov/pubmed/31739517 http://dx.doi.org/10.3390/jcm8111976 |
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author | Nguyen, Dat Tien Pham, Tuyen Danh Batchuluun, Ganbayar Yoon, Hyo Sik Park, Kang Ryoung |
author_facet | Nguyen, Dat Tien Pham, Tuyen Danh Batchuluun, Ganbayar Yoon, Hyo Sik Park, Kang Ryoung |
author_sort | Nguyen, Dat Tien |
collection | PubMed |
description | Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem. |
format | Online Article Text |
id | pubmed-6912332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-69123322020-01-02 Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains Nguyen, Dat Tien Pham, Tuyen Danh Batchuluun, Ganbayar Yoon, Hyo Sik Park, Kang Ryoung J Clin Med Article Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem. MDPI 2019-11-14 /pmc/articles/PMC6912332/ /pubmed/31739517 http://dx.doi.org/10.3390/jcm8111976 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nguyen, Dat Tien Pham, Tuyen Danh Batchuluun, Ganbayar Yoon, Hyo Sik Park, Kang Ryoung Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains |
title | Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains |
title_full | Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains |
title_fullStr | Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains |
title_full_unstemmed | Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains |
title_short | Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains |
title_sort | artificial intelligence-based thyroid nodule classification using information from spatial and frequency domains |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6912332/ https://www.ncbi.nlm.nih.gov/pubmed/31739517 http://dx.doi.org/10.3390/jcm8111976 |
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