Detection algorithm of breast disease based on convolution neural network

ZHAO Jing-xia; QIAN Yu-rong; ZHANG Meng; DU Jiao

Chinese

您当前的位置：

首页 >

文章列表页 >

Detection algorithm of breast disease based on convolution neural network

更新时间：2026-09-04

- Detection algorithm of breast disease based on convolution neural network
- Vol. 36, Issue 7, Pages: 48-53(2019)
- 作者机构：
  
  新疆大学软件学院,新疆,乌鲁木齐,830091
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
赵京霞, 钱育蓉, 张猛, 杜娇. 基于卷积神经网络的乳腺疾病检测算法[J]. 微电子学与计算机, 2019, 36(7): 48-53.

ZHAO Jing-xia, QIAN Yu-rong, ZHANG Meng, et al. Detection algorithm of breast disease based on convolution neural network[J]. 2019, 36(7): 48-53.
赵京霞, 钱育蓉, 张猛, 杜娇. 基于卷积神经网络的乳腺疾病检测算法[J]. 微电子学与计算机, 2019, 36(7): 48-53. DOI：

ZHAO Jing-xia, QIAN Yu-rong, ZHANG Meng, et al. Detection algorithm of breast disease based on convolution neural network[J]. 2019, 36(7): 48-53. DOI：

摘要

为提高计算机辅助乳腺疾病检测的准确率

提出一种基于卷积神经网络的乳腺疾病检测算法.首先

分别从卷积神经网络中提取图像的浅层特征与深层特征并对其进行加权融合; 其次

通过在卷积神经网络中构建空间金字塔池化层

实现了卷积神经网络的多尺度输入; 最后

在Mammographic Image Analysis Society(MIAS)数据集上进行验证实验.实验结果表明

本文提出的乳腺疾病检测算法平均准确率高达94.93%

与其他乳腺疾病检测算法进行对比实验可知

本文提出的乳腺疾病检测算法具有更高的检测准确率.

Abstract

In order to improve the accuracy of computer-aided breast disease detection

a breast disease detection algorithm based on convolution neural network is proposed. Firstly

the shallow and deep features of the images are extracted from the convolution neural network

and then fuse them in a weighted way. Secondly

the multi-scale input of the convolution neural network is realized by using the spatial Pyramid pool algorithm. Finally

the algorithm is tested on the Mammographic Image Analysis Society (MIAS) data set. The experimental results show that the average accuracy rate of the breast disease detection algorithm is up to 93.32%. Compared with other breast disease detection algorithms

the breast disease detection algorithm proposed in this paper has higher detection accuracy.

关键词

Keywords

references

ERTOSUN M G, RUBIN D L. Probabilistic visual search for masses within mammography images using deep learning[C]//IEEE International Conference. Stanford School of Medicine, CA USA, 2015: 1310-1315.

AREVALO J, GONZ ALEZ F A, RAMOS-POLL R, et al. Convolutional neural networks for mammography mass lesion classi?cation[C]//The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, Italy, 2015: 797-800.

吕鸿蒙, 赵地, 迟学斌.基于增强AlexNet的深度学习的阿尔茨海默病的早期诊断[J].计算机科学, 2017, 44(s1):50-60. DOI: 50

王之琼, 王培, 于戈, 等.双侧特征融合的乳腺肿块检测[J].计算机辅助设计与图形学学报, 2015(6):1024-1031. DOI: 1024

PENG W, MAYORGA R V, HUSSEIN E M. An automated confirmatory system for analysis of mammograms[J]. Computer Methods % Programs in Biomedicine, 2016(125):134-144. DOI: 134

SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J].Computer Science, 2014, 9(4):1409-1556. DOI: 1409

HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis % Machine Intelligence, 2015, 37(9):1904-1916. DOI: 1904

JIAO Z, GAO X, WANG Y, et al. A deep feature based framework for breast masses classi?cation[J]. Neurocomputing, 2016: 221-231. DOI:10.1016/j.neucom.2016.02.060.

沈健, 蒋芸, 张亚男, 等.基于边缘检测的多类别医学图像分类方法[J].数据采集与处理, 2016, 31(5):1028-1034. DOI: 1028

周飞燕, 金林鹏, 董军.卷积神经网络研究综述[J].计算机学报, 2017, 40(6):1229-1251. DOI: 1229

胡学伟, 蒋芸, 邹丽, 等.基于邻域关系模糊粗糙集的医学图像分类研究[J].计算机工程与科学, 2016, 38(4):739-746. DOI: 739

何克磊, 史颖欢, 高阳, 等.一种基于原型学习的多示例卷积神经网络[J].计算机学报, 2017, 40(6):1265-1274. DOI: 1265

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of ultra-low power real-time clock circuits

Microservices anomaly detection based on the correlation of monitoring metrics

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Research on assembly action recognition based on MSS-YOLO-MPHands fusion architecture

A review of large model development: technological evolution from BERT to Deepseek

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰