Histopathological Breast-Image Classification With Image Enhancement by Convolutional Neural Network

被引:0
作者
Abdullah-Al Nahid [1 ]
Bin Ali, Ferdous [2 ]
Kong, Yinan [1 ]
机构
[1] Macquarie Univ, Sch Engn, Sydney, NSW 2109, Australia
[2] Khulna Univ, Comp Sci & Engn Discipline, Khulna, Bangladesh
来源
2017 20TH INTERNATIONAL CONFERENCE OF COMPUTER AND INFORMATION TECHNOLOGY (ICCIT) | 2017年
关键词
Classification; Convolutional Neural Network; Retinex Filter; Recall; Specificity;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Finding malignancy from Histopathological images is always a challenging task. So far research has been carried out to classify Histopathological images using various techniques and methods. Recently, the state-of-the art Convolutional Neural Network (CNN) has largely been utilized for natural image classification. In this paper, using the advancement of CNN techniques, we have classified a set of Histopathological Breast images into Benign and Malignant classes, which can save doctors and physicians time and also allow patients a second opinion about the disease.
引用
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页数:6
相关论文
共 8 条
[1]  
[Anonymous], 1958, PSYCHOL REV
[2]  
[Anonymous], 2015, Deep Residual Learning for Image Recognition
[3]  
Blot Michael., 2016, CoRR
[4]  
Hinton, ADV NEURAL INFORM PR, P2012
[5]   Reducing the dimensionality of data with neural networks [J].
Hinton, G. E. ;
Salakhutdinov, R. R. .
SCIENCE, 2006, 313 (5786) :504-507
[6]  
Land E. H., 1971, LIGHTNESS RETINEX TH
[7]  
Szegedy Christian, 2015, PROC IEEE C COMPUT V
[8]  
ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI [10.1016/0020-0255(75)90036-5, 10.1016/0020-0255(75)90046-8]