Deep learning-based diagnosis of thyroid tumors using histopathology images from thyroid nodule capsule

Abstract

Histopathology analysis of thyroid nodule is the current gold standard for the differential diagnosis of thyroid tumors. Deep learning methods have been extensively used for the diagnosis of histopathology images. We look into the possibility of the differential diagnosis of thyroid tumors by analysing histopathology images of thyroid nodule capsules using different deep learning methods namely Residual Network (ResNet), Densely Connected Network (DenseNet) and Vision Transformer (ViT). To evaluate the performance in the classification task, we use various performance metrics including precision, recall, F1-score, and AUROC score. Our study shows the superiority of the histopathology images of thyroid nodule capsules for the differential diagnosis of thyroid tumors compared to histopathology images of thyroid nodules.