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基于中值滤波和残差网络的甲状腺结节检测

LIU Xintong MA Xiaoping LIU Libo

计算机工程与应用2019，Vol.55Issue(13)：254-259,6.

计算机工程与应用2019，Vol.55Issue(13)：254-259,6.DOI:10.3778/j.issn.1002-8331.1804-0131

基于中值滤波和残差网络的甲状腺结节检测

Thyroid Nodule Detection Method Based on Median Filter and Residual Network

LIU Xintong ¹MA Xiaoping ²LIU Libo¹

作者信息

1. School of Information Engineering, Ningxia University, Yinchuan 750021, China 2. Medical Technologic Departments, Yinchuan People’s Hospital, Yinchuan 750002, China
2. Medical Technologic Departments, Yinchuan People’s Hospital, Yinchuan 750002, China
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摘要

Abstract

Thyroid nodule detection plays an important role in medical diagnosis. The traditional machine learning method has many problems, such as the complexity of noise and the difficulty of extracting nodules. A deep learning model is introduced and a thyroid nodule detection method based on median filter and residual network is proposed. The median filtering method based on statistical threshold is used to improve the edge features of the nodules and realize the automatic enhancement of the ultrasonic image. Then, the CNN6-Residual model is constructed to extract and screen the characteristics of the nodules. It has cross layer connection and residual learning in order to reduce the difficulty of network training. The experimental results show that the accuracy of this method is 97.03%, which has high clinical application value.

关键词

统计阈值/中值滤波/残差神经网络/甲状腺结节/特征提取

Key words

statistical threshold/ median filter/ depth residual network/ thyroid nodules/ feature extraction

分类

信息技术与安全科学

引用本文复制引用

LIU Xintong,MA Xiaoping,LIU Libo..基于中值滤波和残差网络的甲状腺结节检测[J].计算机工程与应用,2019,55(13):254-259,6.

基金项目

宁夏自然科学基金(No.NZ17010) （No.NZ17010）

国家自然科学基金(No.61751215) （No.61751215）

西部一流大学科研创新项目(No.ZKZD2017005). （No.ZKZD2017005）

计算机工程与应用

OA北大核心CSCDCSTPCD

ISSN：1002-8331

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