Thyroid cancer, which is one of the top ten most prevalent cancer in Taiwan, can be diagnosed by traditional fine-needle aspiration biopsy or ultrasonic imaging technology cooperated with physicians' clinical experience. Recently, the computer aided diagnosis (CAD) system based on ultrasonic technology is well adopted by the hospitals. However, based on ultrasonic image and human experience, the cancer can only be diagnosed approximately in 80%, the rest thus have to return to invasive biopsy. What is worse, there are still remaining 20% uncertainness after biopsy. In order to increase the detection rate in CAD system, an approach based on shear wave ultrasonic image with computer vision and deep learning technology is developed. Three methods, namely, texture analysis, traditional convolutional neural network (CNN), and densely connected convolutional network (DenseNet), are used for study and comparison. With manual ROI selection, the method based on the DenseNet achieves 88% accuracy, 90.9% sensitivity as well as 96.5% specificity on our testing data, and is thus selected as the kernel used in our thyroid nodule diagnosis system for benign/malignant classification. Furthermore, a semi-automatic user interface has been built, which can diagnose thyroid nodule in real-time clinically and thus improve the physicians' diagnosis accuracy as well as reduce the probability of invasive biopsy.