Ant colony optimization for feature selection and classification of microcalcifications in digital mammograms

被引：0
|
作者：
Karnan, M. [1 ]
Thangavel, K. [2 ]
Sivakuar, R. [3 ]
Geetha, K. [3 ]
机构：
[1] Gandhigram Deemed Univ, Dept Comp Sci, Dindugul, India
[2] Periyar Univ, Dept Comp Sci, Salem, India
[3] Hindusthan Coll Engg & Informat Technol, Coimbatore, Tamil Nadu, India
来源：
2006 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATIONS, VOLS 1 AND 2 | 2007年
关键词：
feature extraction; feature selection; Genetic Algorithm; Ant Colony Optimization; Backpropagation Network and Receiver Operating Characteristic;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with Ant Colony Optimization (BPN-ACO) for classification. And the Receiver Operating Characteristic (ROC) analysis is performed to evaluate the performance of the feature selection methods with their classification results. The proposed algorithms are tested with 114 abnormal images from the Mammography Image Analysis Society (MIAS) database.
引用
收藏
页码：290 / +
页数：2
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