An Improved Deep Learning Approach for Thyroid Nodule Diagnosis

Although thyroid ultrasonography (US) has been widely applied, it is still difficult to distinguish benign and malignant nodules. Currently, convolutional neural network (CNN) based methods have been proposed and shown promising performance for benign and malignant nodules classification. It is known that the US images are usually captured in multi-angles, and the same thyroid in different US images have inconsistent content. However, most of the existing CNN based methods extract features using fixed convolution kernels, which could be a big issue for processing US images. Moreover, fully-connected (FC) layers are usually adopted in CNN, which could cause the loss of inter-pixel relations. In this paper, we propose a new CNN which is integrated with squeeze-and-excitation (SE) module and maximum retention of inter-pixel relations module (CNN-SE-MPR). It can adaptively select features from different US images and preserve the inter-pixel relations. Moreover, we introduce transfer learning to avoid problems such as local optimum and data insufficiency. The proposed network is tested on 407 thyroid US images collected from cooperated hospitals. Confirmed by ablation experiments and the comparison experiments under the state-of-the-art methods, it is shown that our method improves the accuracy of the diagnosis results.