Ultrasonographic Thyroid Nodule Classification Using a Deep Convolutional Neural Network with Surgical Pathology.

Ultrasonography with fine-needle aspiration biopsy is commonly used to detect thyroid cancer. However, thyroid ultrasonography is prone to subjective interpretations and interobserver variabilities. The objective of this study was to develop a thyroid nodule classification system for ultrasonography using convolutional neural networks. Transverse and longitudinal ultrasonographic thyroid images of 762 patients were used to create a deep learning model. After surgical biopsy, 325 cases were confirmed to be benign and 437 cases were confirmed to be papillary thyroid carcinoma. Image annotation marks were removed, and missing regions were recovered using neighboring parenchyme. To reduce overfitting of the deep learning model, we applied data augmentation, global average pooling. And 4-fold cross-validation was performed to detect overfitting. We employed a transfer learning method with the pretrained deep learning model VGG16. The average area under the curve of the model was 0.916, and its specificity and sensitivity were 0.70 and 0.92, respectively. Positive and negative predictive values were 0.90 and 0.75, respectively. We introduced a new fine-tuned deep learning model for classifying thyroid nodules in ultrasonography. We expect that this model will help physicians diagnose thyroid nodules with ultrasonography.