A pre-trained convolutional neural network based method for thyroid nodule diagnosis.
In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method.
School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China.
Jinlian
J
Ma
School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China.
Fa
F
Wu
Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310020, China.
Jiang
J
Zhu
Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou 310022, China.
Dong
D
Xu
School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China. Electronic address: dkong@zju.edu.cn.
Dexing
D
Kong
D044963:Biopsy, Fine-Needle; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D016606:Thyroid Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014463:Ultrasonography / Q000379:methods*
Classification; Convolutional neural network; Diagnosis; Feature extraction; Thyroid nodule; Ultrasound image