Differentiated
thyroid cancer (DTC) from follicular epithelial cells is the most common form of
thyroid cancer. Beyond the common
papillary thyroid carcinoma (PTC), there are a number of rare but difficult-to-diagnose pathological classifications, such as
follicular thyroid carcinoma (
FTC). We employed deep convolutional neural networks (CNNs) to facilitate the clinical diagnosis of differentiated
thyroid cancers. An image dataset with thyroid ultrasound images of 421 DTCs and 391 benign patients was collected. Three CNNs (InceptionV3, ResNet101, and VGG19) were retrained and tested after undergoing transfer learning to classify malignant and benign thyroid
tumors. The enrolled cases were classified as PTC,
FTC, follicular variant of PTC (FVPTC), Hürthle cell
carcinoma (HCC), or benign. The accuracy of the CNNs was as follows: InceptionV3 (76.5%), ResNet101 (77.6%), and VGG19 (76.1%). The sensitivity was as follows: InceptionV3 (83.7%), ResNet101 (72.5%), and VGG19 (66.2%). The specificity was as follows: InceptionV3 (83.7%), ResNet101 (81.4%), and VGG19 (76.9%). The area under the curve was as follows: Incep-tionV3 (0.82), ResNet101 (0.83), and VGG19 (0.83). A comparison between performance of physicians and CNNs was assessed and showed significantly better outcomes in the latter. Our results demonstrate that retrained deep CNNs can enhance diagnostic accuracy in most DTCs, including follicular
cancers.