SeCED-FS: a new approach for the classification and discovery of significant regions in medical images

Authors:
Hui Li;Hanhu Wang;Mei Chen;Teng Wang;Xuejian Wang
Affiliations:
Department of Computer Science & Technology, Guizhou University, Guiyang, P.R. China;Department of Computer Science & Technology, Guizhou University, Guiyang, P.R. China;Department of Computer Science & Technology, Guizhou University, Guiyang, P.R. China;Department of Computer Science & Technology, Guizhou University, Guiyang, P.R. China;Department of Radiology, Affiliated Hospital of Guiyang Medical College, Guiyang, P.R. China
Venue:
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Year:
2007

Citing 9
Cited 1

Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining lesion-deficit associations in a brain image database

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Ensembling neural networks: many could be better than all

Artificial Intelligence
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rule-Based Labeling of CT Head Image

AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
A Bayesian network classifier with inverse tree structure for voxelwise magnetic resonance image analysis

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Classification of brain glioma by using SVMs bagging with feature selection

BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble

IEEE Transactions on Information Technology in Biomedicine
Lung cancer cell identification based on artificial neural network ensembles

Artificial Intelligence in Medicine

An ensemble method for medicine best selling prediction

FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel diagnosis method named SeCED-FS is proposed in this paper. The method combines the clusterer ensemble and feature selection technique to improve the diagnosis performance. At first, selective clusterer ensemble with feature selection technique is utilized to perform the classification of medical images in the two-level architecture. Then, the Regions of Interest in positively identified image are outlined by using an ensemble of Fuzzy C-Means algorithm. Case studies on real data experiments show that, the SeCED-FS holds the improved generalization ability and achieved a satisfactory result not only in the accuracy of classification but also correctly labeling the significant regions.