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Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features

Abstract: 

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-sample-first 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%.

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Paper Details

Authors:
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun
Submitted On:
8 March 2017 - 3:35am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun
Paper Code:
BISP-P1.8
Document Year:
2017
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Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features

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[1] Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun, "Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1701. Accessed: Sep. 20, 2017.
@article{1701-17,
url = {http://sigport.org/1701},
author = {Tianjiao Liu; Shuaining Xie; Jing Yu; Lijuan Niu; Weidong Sun },
publisher = {IEEE SigPort},
title = {Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features},
year = {2017} }
TY - EJOUR
T1 - Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features
AU - Tianjiao Liu; Shuaining Xie; Jing Yu; Lijuan Niu; Weidong Sun
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1701
ER -
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. (2017). Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features. IEEE SigPort. http://sigport.org/1701
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun, 2017. Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features. Available at: http://sigport.org/1701.
Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. (2017). "Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features." Web.
1. Tianjiao Liu, Shuaining Xie, Jing Yu, Lijuan Niu, Weidong Sun. Classification of Thyroid Nodules in Ultrasound Images Using Deep Model Based Transfer Learning and Hybrid Features [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1701