A Non-Invasive Follicular Thyroid Cancer Risk Prediction System Based on Deep Hybrid Multi-feature Fusion Network

A non-invasive assessment of the risk of benign and malignant follicular thyroid cancer is invaluable in the choice of treatment options. The extraction and fusion of multidimensional features from ultrasound images of follicular thyroid cancer is decisive in improving the accuracy of identifying benign and malignant thyroid cancer. This paper presents a non-invasive preoperative benign and malignant risk assessment system for follicular thyroid cancer, based on the proposed deep feature extraction and fusion of ultrasound images of follicular thyroid cancer.

First, this study uses a convolution neural network (CNN) to obtain a global feature map of the image, and the fusion of global features cropped to local features to identify tumor images. Secondly, this tumour image is also extracted by googleNet and ResNet respectively to extract features and recognize the image. Finally, we employ an averaging algorithm to obtain the final recognition results.

The experimental results show that the method proposed in this study achieved 89.95% accuracy, 88.46% sensitivity, 91.30% specificity and an AUC value of 96.69% in the local dataset obtained from Peking University Shenzhen Hospital, all of which are far superior to other models.

In this study, a non-invasive risk prediction system is proposed for ultrasound images of thyroid follicular tumours. We solve the problem of unbalanced sample distribution by means of an image enhancement algorithm. In order to obtain enough features to differentiate ultrasound images, a three-branched feature extraction network was designed in this study, and a balance of sensitivity and specificity is ensured by an averaging algorithm.