Issue |
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|
|
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Article Number | 03021 | |
Number of page(s) | 5 | |
Section | Medical Biology and Medical Signal Processing | |
DOI | https://doi.org/10.1051/e3sconf/202018503021 | |
Published online | 01 September 2020 |
Automatic Malignant Thyroid Nodule Recognition in Ultrasound Images based on Deep Learning
1 Key Laboratory of Precision Opto-mechatronics Technology Opto-eletronics Engineering, Beihang University Beijing, China
2 Department of Ultrasound, Peking University Third Hospital, Beijing, China
3 Department of general surgery, Peking University Third Hospital.
* Corresponding author: wangr@buaa.edu.cn
As the most common malignancy in the endocrine system, thyroid cancer is usually diagnosed by discriminating the malignant nodules from the benign ones using ultrasonography, whose interpretation results primarily depends on the subjectivity judgement of the radiologists. In this study, we propose a novel cascade deep learning model to achieve automatic objective diagnose during ultrasound examination for assisting radiologists in recognizing benign and malignant thyroid nodules. First, the simplified U-net is employed to segment the region of interesting (ROI) of the thyroid nodules in each frame of the ultrasound image automatically. Then, to alleviate the limitation that medical training data are relatively small in size, the improved Conditional Variational Auto-Encoder (CVAE) learning the probability distribution of ROI images is trained to generate new images for data augmentation. Finally, ResNet50 is trained with both original and generated ROI images. As consequence, the deep learning model formed by the trained U-net and trained Resnet-50 cascade can achieve malignant thyroid nodule recognition with the accuracy of 87.4%, the sensitivity of 92%, and the specificity of 86.8%.
© The Authors, published by EDP Sciences, 2020
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