Abstract
Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. Although US is non-invasive and can accurately differentiate benign and malignant thyroid nodules, it is subjective and its results inevitably lack reproducibility. Therefore, to provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. The diagnostic performances of 6 radiologists and 3 representative results obtained from the proposed CADx system were compared and analyzed.
Highlights
Thyroid nodules are a common clinical problem
Advances in high-resolution ultrasonography (US) along with increased access to health check-up services and increased medical surveillance have led to a massive escalation in the number of detected thyroid nodules, especially small thyroid nodules, and thyroid nodules have been detected in up to 68% of adults[1]
While interobserver variability (IOV) is very low among experienced physicians[3], poor agreement was documented when US findings of thyroid nodules were interpreted by less experienced physicians[4]
Summary
Thyroid nodules are a common clinical problem. Ultrasonography (US) is the main tool used to sensitively diagnose thyroid cancer. To provide objective and reliable information for US assessment, we developed a CADx system that utilizes convolutional neural networks and the machine learning technique. Many classifiers are variations of Support Vector Machine (SVM), decision tree, K-nearest neighbor, etc[11]. Both feature extraction techniques and classification methods have been widely used for thyroid US images[5,12–21]. Deep learning has attracted attention to recent image classification problems by showing outstanding results in the ImageNet Large Scale Visual Recognition Competition (ILSVRC). In the 2010s, feature extraction based on deep learning was introduced as big data began to be utilized in the medical field[22–24]. The deep learning method generates non-handcrafted features from original data and acts as a classifier.
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