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Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decisi...

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Autores principales: Vadhiraj, Vijay Vyas, Simpkin, Andrew, O’Connell, James, Singh Ospina, Naykky, Maraka, Spyridoula, O’Keeffe, Derek T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225215/
https://www.ncbi.nlm.nih.gov/pubmed/34074037
http://dx.doi.org/10.3390/medicina57060527
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author Vadhiraj, Vijay Vyas
Simpkin, Andrew
O’Connell, James
Singh Ospina, Naykky
Maraka, Spyridoula
O’Keeffe, Derek T.
author_facet Vadhiraj, Vijay Vyas
Simpkin, Andrew
O’Connell, James
Singh Ospina, Naykky
Maraka, Spyridoula
O’Keeffe, Derek T.
author_sort Vadhiraj, Vijay Vyas
collection PubMed
description Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.
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spelling pubmed-82252152021-06-25 Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques Vadhiraj, Vijay Vyas Simpkin, Andrew O’Connell, James Singh Ospina, Naykky Maraka, Spyridoula O’Keeffe, Derek T. Medicina (Kaunas) Article Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice. MDPI 2021-05-24 /pmc/articles/PMC8225215/ /pubmed/34074037 http://dx.doi.org/10.3390/medicina57060527 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vadhiraj, Vijay Vyas
Simpkin, Andrew
O’Connell, James
Singh Ospina, Naykky
Maraka, Spyridoula
O’Keeffe, Derek T.
Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
title Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
title_full Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
title_fullStr Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
title_full_unstemmed Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
title_short Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
title_sort ultrasound image classification of thyroid nodules using machine learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225215/
https://www.ncbi.nlm.nih.gov/pubmed/34074037
http://dx.doi.org/10.3390/medicina57060527
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