@inproceedings{e0a62923fcd2470ea0d6c257a2e9b6ed,
title = "Hybrid feature selection and tumor identification in brain MRI using swarm intelligence",
abstract = "Demand for automatic classification of Brain MRI (Magnetic Resonance Imaging) in the field of Diagnostic Medicine is rising. Feature Selection of Brain MRI is critical and it has a great influence on the classification outcomes, however selecting optimal Brain MRI features is difficult. Particle Swarm Optimization (PSO) is an evolutionary meta-heuristic approach that has shown great potential in solving NP-hard optimization problems. In this paper MRI feature selection is achieved using Discrete Binary Particle Swarm Optimization (DBPSO). Classification of normal and abnormal Brain MRI is carried out using two different classifiers i.e. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Experimental results show that the proposed approach reduces the number of features and at the same time it achieves high accuracy level. PSO-SVM is observed to achieve high accuracy level using minimum number of selected features.",
keywords = "Brain Magnetic Resonance Imaging, Classifier, Feature Selection, K-Nearest Neighbor, Particle Swarm Optimization, Support Vector Machine",
author = "Rehman, {Atiq Ur} and Aasia Khanum and Arslan Shaukat",
year = "2013",
doi = "10.1109/FIT.2013.17",
language = "English",
isbn = "9781479922932",
series = "Proceedings - 11th International Conference on Frontiers of Information Technology, FIT 2013",
pages = "49--54",
booktitle = "Proceedings - 11th International Conference on Frontiers of Information Technology, FIT 2013",
note = "11th International Conference on Frontiers of Information Technology, FIT 2013 ; Conference date: 16-12-2013 Through 18-12-2013",
}