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Article

Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

1
School of Medicine, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33 Galway, Ireland
2
Health Innovation Via Engineering Laboratory, Cúram SFI Research Centre for Medical Devices, Lambe Institute for Translational Research, National University of Ireland Galway, H91 TK33 Galway, Ireland
3
School of Mathematics, Statistics and Applied Maths, National University of Ireland, H91 TK33 Galway, Ireland
4
Division of Endocrinology, Department of Medicine, University of Florida, Gainesville, FL 3210, USA
5
Division of Endocrinology and Metabolism, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
6
Medicine Section, Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, USA
7
Lero, SFI Centre for Software Research, National University of Ireland Galway, H91 TK33 Galway, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Stephen Usala
Received: 26 March 2021 / Revised: 10 May 2021 / Accepted: 18 May 2021 / Published: 24 May 2021
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. View Full-Text
Keywords: computer aided diagnostics; CAD; artificial intelligence; AI; digital health; TI-RADS; big data; ANN; SVM; malignant; benign; cancer computer aided diagnostics; CAD; artificial intelligence; AI; digital health; TI-RADS; big data; ANN; SVM; malignant; benign; cancer
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MDPI and ACS Style

Vadhiraj, V.V.; Simpkin, A.; O’Connell, J.; Singh Ospina, N.; Maraka, S.; O’Keeffe, D.T. Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques. Medicina 2021, 57, 527. https://0-doi-org.brum.beds.ac.uk/10.3390/medicina57060527

AMA Style

Vadhiraj VV, Simpkin A, O’Connell J, Singh Ospina N, Maraka S, O’Keeffe DT. Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques. Medicina. 2021; 57(6):527. https://0-doi-org.brum.beds.ac.uk/10.3390/medicina57060527

Chicago/Turabian Style

Vadhiraj, Vijay V., Andrew Simpkin, James O’Connell, Naykky Singh Ospina, Spyridoula Maraka, and Derek T. O’Keeffe 2021. "Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques" Medicina 57, no. 6: 527. https://0-doi-org.brum.beds.ac.uk/10.3390/medicina57060527

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