SThy-Net: a feature fusion-enhanced dense-branched modules network for small thyroid nodule classification from ultrasound images

Abstract

Deep learning studies of thyroid nodule classification from ultrasound (US) images have focused mainly on nodules with diameters > 1 cm. However, small thyroid nodules measuring = 1 cm, especially nodules with high-risk stratification, are prevalent in the population but without enough focus, including papillary thyroid microcarcinoma (PTMC) as their common malignant type. Additionally, small nodules with high-risk stratification are difficult for physicians to diagnose from US images due to their atypical features. In this work, we propose a small thyroid nodule classification network (SThy-Net) to classify benign and PTMC small thyroid nodules with high-risk stratification from US images. We design two main components, a dense-branched module and a Gaussian-enhanced feature fusion module, to help recognize small thyroid nodules. To our knowledge, this work is the first to address the challenging task of classifying small thyroid nodules using US images. Our SThy-Net achieves as high accuracy as 87.4% compared to five state-of-the-art thyroid nodule diagnosis studies, several state-of-the-art deep learning models, and three radiologists. From visual explainability, our network shows an intuitive feature extraction method and consistency with US image analysis of radiologists. The results suggest that our network has the potential to be an affordable tool for radiologists to diagnose small nodules with high-risk stratification in clinical practice.