This paper proposed a non-segmentation radiological method for classification of benign and malignant thyroid tumors using B mode ultrasound data. This method aimed to combine the advantages of morphological information provided by ultrasound and convolutional neural networks in automatic feature extraction and accurate classification. Compared with the traditional feature extraction method, this method directly extracted features from the data set without the need for segmentation and manual operations. 861 benign nodule images and 740 malignant nodule images were collected for training data. A deep convolution neural network VGG-16 was constructed to analyze test data including 100 malignant nodule images and 109 benign nodule images. A nine fold cross validation was performed for training and testing of the classifier. The results showed that the method had an accuracy of 86.12%, a sensitivity of 87%, and a specificity of 85.32%. This computer-aided method demonstrated comparable diagnostic performance with the result reported by an experienced radiologist based on American college of radiology thyroid imaging reporting and data system (ACR TI-RADS) (accuracy: 87.56%, sensitivity: 92%, and specificity: 83.49%). The automation advantage of this method suggested application potential in computer-aided diagnosis of thyroid cancer.