Feature selection and thyroid nodule classification using transfer learning

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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 and specific features to the classification. A CNN model trained with ImageNet data is transferred to the ultrasound image domain, to generate semantic deep features under small sample condition. Then, we combine those deep features with conventional features such as Histogram of Oriented Gradient (HOG) and Scale Invariant Feature Transform (SIFT) together to form a hybrid feature space. Furthermore, to make the general deep features more pertinent to our problem, a feature subset selection process is employed for the hybrid nodule classification, followed by a detailed discussion on the influence of feature number and feature composition method. Experimental results on 1037 images show that the accuracy of our proposed method is 0.929, which outperforms other relative methods by over 10%.