Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 74-79.doi: 10.6040/j.issn.1672-3961.0.2018.273

• Machine Learning & Data Mining • Previous Articles     Next Articles

Computer aided diagnosis method for breast cancer based on AlexNet and ensemble classifiers

Xiaoxiong HOU1,2(),Xinzheng XU1,2,*(),Jiong ZHU1,Yanyan GUO1   

  1. 1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
    2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence, Nanning 530006, Guangxi, China
  • Received:2018-07-06 Online:2019-04-20 Published:2019-04-19
  • Contact: Xinzheng XU E-mail:961458517@qq.com;xuxinzh@163.com
  • Supported by:
    国家自然科学基金项目(61672522);广西高校复杂系统与智能计算重点实验室开放课题重点项目(2017CSCI01)

RichHTML

13

PDF (PC)

515

Abstract:

In order to solve the manual feature extraction of medical images in computer aided diagnosis, Alexnet was pre-trained on the ImageNet dataset, and feature extraction was performed on the medical image based on Alexnet with transfer learning. The ensemble learning method was used to train the classifier to classify and obtain a better classification effect than the single classifier. The results showed that the AUC(area under curve) of Alexnet deep learning model and random forest ensemble classifier reached 0.87±0.03, and the effect of the ensemble classifier was better than that of the single classifier in the same network depth.

Key words: medical image analysis, deep learning, convolutional neural network, computer aided diagnosis, ensemble classifiers

CLC Number: 

  • TP391

Fig.1

The workflow chart of CAD"

Fig.2

Steps carried out in preprocessing of mammography"

Fig.3

Structure of Alexnet network"

Table 1

Corresponding test AUC of different depthsCNN with SVM classifier"

特征提取模型 AUC
CNN(2层) 0.76±0.05
CNN(3层) 0.82±0.03
Alexnet 0.84±0.06

Table 2

Corresponding test AUC of Alexnet model withdifferent classifier"

分类器 AUC值
SVM 0.84±0.06
Logistic Regression 0.83±0.03
Softmax 0.85±0.04

Table 3

Test AUC values of Alexnet model withensemble classifier"

分类器 AUC值
Softmax 0.85±0.04
Random Forest 0.87±0.03
1 BUNGE R E , HERMAN C L . Usage of diagnostic imaging procedures: a nationwide hospital study[J]. Radiology, 1987, 163 (2): 569- 573.
doi: 10.1148/radiology.163.2.3550886
2 SLUIMER I , SCHILHAM A , PROKOP M , et al. Computer analysis of computed tomography scans of the lung: a survey[J]. IEEE Trans Med Imaging, 2006, 25 (4): 385- 405.
doi: 10.1109/TMI.2005.862753
3 QUEKEL L G , KESSELS A G , GOEI R , et al. Miss rate of lung cancer on the chest radiograph in clinical practice[J]. Chest, 1999, 115 (3): 720- 724.
doi: 10.1378/chest.115.3.720
4 LI F , SONE S , ABE H , et al. Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings[J]. Radiology, 2002, 225 (3): 673- 683.
doi: 10.1148/radiol.2253011375
5 DOI K , MACMAHON H , KATSURAGAWA S , et al. Computer-aided diagnosis in radiology: potential and pitfalls[J]. European Journal of Radiology, 1999, 31 (2): 97- 109.
doi: 10.1016/S0720-048X(99)00016-9
6 GIGER M L , CHAN H P , BOONE J . Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM[J]. Medical Physics, 2008, 35 (12): 5799- 5820.
doi: 10.1118/1.3013555
7 LI Q , LI F , SUZUKI K , et al. Computer-aided diagnosis in thoracic CT[J]. Seminars in Ultrasound, CT and MRI, 2005, 26 (5): 357- 363.
doi: 10.1053/j.sult.2005.07.001
8 HINTON G E , OSINDERO S , TEH Y W . A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18 (7): 1527- 1554.
doi: 10.1162/neco.2006.18.7.1527
9 MCLOUGHLIN K J , BONES P J , KARSSEMEIJER N . Noise equalization for detection of microcalcification clusters in direct digital mammogram images[J]. IEEE Transactions on Medical Imaging, 2004, 23 (3): 313- 320.
doi: 10.1109/TMI.2004.824240
10 TALHA M , SULONG G B , JAFFAR A . Preprocessing digital breast mammograms using adaptive weighted frost filter[J]. Biomedical Research, 2016, 27 (4): 1407- 1412.
11 PONRAJ D N , JENIFER M E , POONGODI P , et al. A survey on the preprocessing techniques of mammogram for the detection of breast cancer[J]. Journal of Emerging Trends in Computing & Information Sciences, 2011, 2 (12): 656- 664.
12 MAKANDAR A , HALALLI B . Pre-processing of mammography image for early detection of breast bancer[J]. International Journal of Computer Applications, 2016, 144 (3): 11- 15.
doi: 10.5120/ijca2016910153
13 RAMANI R , VANITHA N S , VALARMATHY S . The pre-processing techniques for breast cancer detection in mammography images[J]. International Journal of Image Graphics & Signal Processing, 2013, 5 (5): 47- 54.
14 MUSTRA M , GRGIC M , RANGAYYAN R M . Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms[J]. Medical & Biological Engineering & Computing, 2016, 54 (7): 1003- 1024.
15 MAKANDAR A , HALALLI B . Breast cancer image enhancement using median filter and clahe[J]. International Journal of Scientific & Engineering Research, 2015, 6 (4): 462- 465.
16 KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems. Vancouver, Canada: Neural Information Processing Systems Foundation, 2012: 1097-1105.
17 DONAHUE J, JIA Y, VINYALS O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition[J]. arXiv: 1310.1531 [cs.CV], 2013: 1-10.
18 RUSSAKOVSKY O , DENG J , SU H , et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2014, 115 (3): 211- 252.
19 BREIMAN L . Random Forest[J]. Machine Learning, 2001, 45 (1): 5- 32.
20 BREIMAN L . Bagging predictors[J]. Machine Learning, 1996, 24 (2): 123- 140.
21 AREVALO J , GONZ LEZ F A , RAMOS-POLL N R , et al. Representation learning for mammography mass lesion classification with convolutional neural networks[J]. Computer Methods & Programs in Biomedicine, 2016, 127 (C): 248- 257.
[1] Lizhao LI,Guoyong CAI,Jiao PAN. A microblog rumor events detection method based on C-GRU [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 102-106, 115.
[2] Chengbin ZHANG,Hui ZHAO,Zongyu CAO. The vulnerability mining method for KWP2000 protocol based on deep learning and fuzzing [J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 17-22.
[3] Mengmeng LIANG,Tao ZHOU,Yong XIA,Feifei ZHANG,Jian YANG. Lung tumor images recognition based on PSO-ConvK convolutional neural network [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 77-84.
[4] Pu ZHANG,Chang LIU,Yong WANG. Suggestion sentence classification model based on feature fusion and ensemble learning [J]. Journal of Shandong University(Engineering Science), 2018, 48(5): 47-54.
[5] LI Yuxin, PU Yuanyuan, XU Dan, QIAN Wenhua, LIU Hejuan. Image aesthetic quality evaluation based on embedded fine-tune deep CNN [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 60-66.
[6] HE Zhengyi, ZENG Xianhua, GUO Jiang. An ensemble method with convolutional neural network and deep belief network for gait recognition and simulation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 88-95.
[7] XIE Zhifeng, WU Jiaping, MA Lizhuang. Chinese financial news classification method based on convolutional neural network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 34-39.
[8] ZHAO Yanxia, WANG Xizhao. Multipurpose zero watermarking algorithm for color image based on SVD and DCNN [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 25-33.
[9] TANG Leshuang, TIAN Guohui, HUANG Bin. An object fusion recognition algorithm based on DSmT [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 50-56.
[10] ZHOU Funa, GAO Yulin, WANG Jiayu, WEN Chenglin. Early diagnosis and life prognosis for slowlyvarying fault based on deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 30-37.
[11] HE Zhengyi, ZENG Xianhua, QU Shengwei, WU Zhilong. The time series prediction model based on integrated deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 40-47.
[12] ZHENG Yi, ZHU Chengzhang. A prediction method of atmospheric PM2.5 based on DBNs [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2014, 44(6): 19-25.
[13] XU Shan-shan, LIU Ying-an*, XU Sheng. Wood defects recognition based on the convolutional neural network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(2): 23-28.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] LI Liang, LUO Qiming, CHEN Enhong. Graph-based ranking model for object-level search
[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 15 -21 .
[3] LI Ke,LIU Chang-chun,LI Tong-lei . Medical registration approach using improved maximization of mutual information[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 107 -110 .
[4] JI Tao,GAO Xu/sup>,SUN Tong-jing,XUE Yong-duan/sup>,XU Bing-yin/sup> . Characteristic analysis of fault generated traveling waves in 10 Kv automatic blocking and continuous power transmission lines[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 111 -116 .
[5] QIN Tong, SUN Fengrong*, WANG Limei, WANG Qinghao, LI Xincai. 3D surface reconstruction using the shape based interpolation guided by maximal discs[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(3): 1 -5 .
[6] SUN Guohua, WU Yaohua, LI Wei. The effect of excise tax control strategy on the supply chain system performance[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 63 -68 .
[7] SUN Weiwei， WANG Yuzhen. Finite gain stabilization of singlemachine infinite bus system subject to saturation[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 69 -76 .
[8] SUN Dianzhu, ZHU Changzhi, LI Yanrui. [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 84 -86 .
[9] . [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 92 -95 .
[10] HAO Ranhang,CHEN Shouyu . The theory, model and method of water resources evaluationombining quantity with quality[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(3): 46 -50 .
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd