Paper Info
Title
An enhanced decision support system for breast tumor identification in screening mammograms using combined classifier
Abstract
An enhanced Computer Aided Clinical Decision Making System using Multiple classifier systems (MCSs) based on the combination of a set of different classifiers for classifying the breast tumor as malignant and benign has been developed and presented in this paper. The Multilayer Back Propagation Neural Network (MBPN), Radial-Basis-Function Neural Network (RBFNN), Asymmetrical Support Vector Machine (ASVM) and combined classifier with major voting method, behaviour-knowledge space method have been used to classify the tumor. The multiple features with optimal feature selection and combined classifier with behaviour-knowledge space method is found to have the accuracy 99.97%. The performance of the proposed clinical decision support system has been estimated and found that this hybrid system will provide valuable information to the physicians in clinical pathology.
Year
DOI
Venue
2010
10.1145/1741906.1742088
ICWET
Keywords
Field
DocType
breast tumor,different classifier,hybrid system,clinical pathology,behaviour-knowledge space method,enhanced decision support system,major voting method,combined classifier,propagation neural network,breast tumor identification,multiple classifier system,radial-basis-function neural network,clinical decision support system,feature extraction,majority voting,support vector machine,feature selection
Pattern recognition,Feature selection,Computer science,Support vector machine,Decision support system,Feature extraction,Artificial intelligence,Clinical decision support system,Artificial neural network,Classifier (linguistics),Hybrid system,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
M. Suganthi100.34
M. Madheswaran210215.57