A Hybrid Deep Learning and Handcrafted Features based Approach for Thyroid Nodule Classification in Ultrasound Images

Content Provider	Scilit
Author	Xie, Jiahao Guo, Lehang Zhao, Chongke Li, Xiaolong Luo, Ye Jianwei, Lu
Copyright Year	2020
Description	Journal: Journal of Physics: Conference Series With the increasing incidence rate of thyroid cancer, the diagnosis of thyroid nodules has become an important task. In this paper, we designed a deep neural network (DNN) to classify whether a thyroid nodule is benign or malignant, and proposed a structure which combines local binary pattern (LBP) with deep learning. Our method mitigates the effects of overfitting in medical image diagnosis tasks. With well-designed transfer leaning, we achieve an accuracy of 85% on our own ultrasound thyroid dataset. To ensure the reliability of our experiments, all examples are estimated by experts in Shanghai Tenth People’s Hospital using fine needle analysis (FNA), which is a gold standard for thyroid nodules diagnosis. The experimental results show that combinations of the traditional medial image features can help the deep learning network get more semantic information from low-level inputs.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1693/1/012160/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1693/1/012160
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1693
Language	English
Publisher	IOP Publishing
Publisher Date	2020-12-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Imaging Science Diagnosis of Thyroid Nodules
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy