Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features

Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound images based on the convolution neural networks (CNNs), try to introduce more meaningful semantic features to the classification. Firstly, a CNN model trained with a massive natural dataset is transferred to the ultrasound image domain, to generate semantic deep features and handle the small sample problem. Then, we combine those deep features with conventional features such as Histogram of Oriented Gradient (HOG) and Local Binary Patterns (LBP) together, to form a hybrid feature space. Finally, a positive-samplefirst majority voting and a feature-selected based strategy are employed for the hybrid classification. Experimental results on 1037 images show that the accuracy of our proposed method is 0.931, which outperformed other relative methods by over 10%.