Title :
Improved PSO-SVM based disease detection in medical images processing
Author :
Jiang, Huiyan ; Zou, Lingbo
Author_Institution :
Software Coll., Northeastern Univ., Shenyang, China
fDate :
Nov. 29 2011-Dec. 1 2011
Abstract :
Support vector machine is a widely used tool in the field of image processing and pattern recognition. The parameters selection plays a significant role in support vector machine(SVM). This paper proposed an improved parameter optimization method based on traditional PSO optimizing algorithm by changing the fitness function in the traditional process. And this method has achieved better results which reflected in the ROC curves in medical images classification.
Keywords :
image classification; medical image processing; particle swarm optimisation; support vector machines; ROC curves; fitness function; image processing; improved PSO-SVM based disease detection; medical images classification; parameter optimization method; parameters selection; pattern recognition; support vector machine; Accuracy; Cancer; Classification algorithms; Diseases; Kernel; Liver; Support vector machines; ROC curve; fitness function; images processing; support vector machines;
Conference_Titel :
Computer Sciences and Convergence Information Technology (ICCIT), 2011 6th International Conference on
Conference_Location :
Seogwipo
Print_ISBN :
978-1-4577-0472-7