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Automatic Lung CT Classification using LogitBoost Algorithm Optimized by PSO
Current Issue
Volume 1, 2014
Issue 1 (March)
Pages: 1-4   |   Vol. 1, No. 1, March 2014   |   Follow on         
Paper in PDF Downloads: 22   Since Aug. 28, 2015 Views: 1518   Since Aug. 28, 2015
Authors
[1]
Saeid Fazli , Research Institute of Modern Biological Techniques, University of Zanjan, Zanjan, Iran.
[2]
Mitra Jafari , Department of Electrical Eng, University of Zanjan, Zanjan, Iran.
Abstract
Automated systems based on machine learning present more accurate diagnosis and easy analysis of medical images. In real world applications especially in medical applications we are usually faced with the problem of imbalanced data sets. In lung CT nodule detection systems, nodules are a part of minority class and other non-nodule organs are a member of the majority class. So to detect nodules without ignoringthem we should improve the predictive accuracies over the minority class. Conventional machine learning classifiers have high predictive accuracy over the majority class and less predictive accuracy over the class with minor members. Ensemble of classifiers is one of the most important solutions.In this work researchers used the LogitBoost ensemble classifier to diagnosis nodules from non-nodule objects. LogitBoost optimized the AdaBoostM1 by minimizing the binomial deviance which causes incorrect classification over the minority observations. LogitBoost can give better average accuracy than AdaBoostM1 for data with poorly separable classes. One of the problems in ensemble classifiers is estimating the cost parameters. In this paper Particle Swarm Optimization (PSO) algorithm was used to optimize these parameters. The extracted features from lung nodules were applied to the proposed optimized LogitBoost algorithm as its predictor variables. Then the data set was randomly divided into two sets: 80% as the training set and the remaining 20% as testing set. Simulation results revealedthat the proposed algorithm could detect nodules with high performance of AUC=86%.
Keywords
Computer Aided Diagnosis (CAD), Lung Nodule, Ensemble Classification, Learning Classifiers, LogitBoost, EstimateParameter, PSO
Reference
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M. Gomathi and Dr. P. Thangaraj“Automated CAD for Lung Nodule Detection using CT Scans,”, InPress,International Conference on Data Storage and Data Engineering,2010.
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[5]
Verma, B., Rahman, A., "Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on Ensemble Classifier Learning", IEEE Trans. Knowledge and Data Engineering, Vol. 24, No. 4, pp. 605-618, 2012.
[6]
Kennedy, J., Eberhart, R., "Particle Swarm Optimization", IEEE Int. Conf. on Neural Networks, Piscataway, NJ, Vol. 4, pp. 1942-1948, 1995.
[7]
Sedighizadeh, D., Masehian, E., "Particle SwarmOptimization Methods, Taxonomy and Applications", Int.J. Computer Theory and Engineering (IJCTE), Vol. 1,No. 5, pp. 486-502, 2009.
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