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Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques
- Source :
- Medicina, Volume 57, Issue 6, Medicina, Vol 57, Iss 527, p 527 (2021)
- Publication Year :
- 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.
- Subjects :
- Thyroid nodules
Medicine (General)
computer aided diagnostics
SVM
digital health
Colombia
Machine learning
computer.software_genre
Sensitivity and Specificity
Article
Machine Learning
R5-920
malignant
big data
Median filter
Medicine
Humans
cancer
Segmentation
CAD
Diagnosis, Computer-Assisted
Thyroid Nodule
Thyroid cancer
Ultrasonography
TI-RADS
Artificial neural network
business.industry
Nodule (medicine)
General Medicine
medicine.disease
artificial intelligence
Support vector machine
Statistical classification
AI
Artificial intelligence
medicine.symptom
benign
business
ANN
computer
Subjects
Details
- ISSN :
- 16489144
- Volume :
- 57
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Medicina (Kaunas, Lithuania)
- Accession number :
- edsair.doi.dedup.....370773ec33fa47c9b75f4d9b8780ede7