A Deep Learning-Based Cascade Automatic Classification System for Malignant Thyroid Nodule Recognition in Ultrasound Image

As the most common malignancy in the endocrine system, thyroid cancer is often determined based on ultrasound images by professional radiologists based on ultrasound images. In this study, a cascade automatic classification system (CACS) is proposed to accurately achieve automatic segmentation and classification of thyroid nodules, reducing over-reliance on experienced doctors in the diagnosis. Firstly, the Res-UNet is used to segment the region of interest (ROI) of thyroid nodules in ultrasound images. Then, to overcome the limitations of the small sample size of medical training data as well as category imbalance, the modified Conditional Variational Auto-Encoder (CVAE), learning the probability distribution of the ultrasound images, is trained to produce new medical ultrasound images for ROI data augmentation. Finally, we perform the classification task by a three-branch ensemble network combined with clinical diagnostic features of thyroid nodule such as the aspect ratio, internal and external echoes, and edges. The proposed CACS can identify benign and malignant thyroid nodules with an accuracy of 0.892 and an AUC of 0.887, according to testing on real patient data.