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Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Authors :
Vijay Vyas Vadhiraj
Andrew Simpkin
James O’Connell
Naykky Singh Ospina
Spyridoula Maraka
Derek T. O’Keeffe
Source :
Medicina, Vol 57, Iss 6, p 527 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
16489144 and 1010660X
Volume :
57
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Medicina
Publication Type :
Academic Journal
Accession number :
edsdoj.35c9f241611248e3a7ad55c779f2e147
Document Type :
article
Full Text :
https://doi.org/10.3390/medicina57060527