@inbook{8e9e036aa76843bbb6f4c68e967545ea,
title = "Texture feature selection using GA for classification of human brain MRI scans",
abstract = "Intelligent Medical Image Analysis plays a vital role in identification of various pathological conditions. Magnetic Resonance Imaging (MRI) is a useful imaging technique that is widely used by physicians to investigate dif-ferent pathologies. Increase in computing power has introduced Computer Aided Diagnosis (CAD) which can effectively work in an automated environ-ment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI classifier targeting two main objectives, the first is to achieve maximum clas-sification accuracy and secondly to minimize the number of features for clas-sification. Two different machine learning algorithms are enhanced with a feature selection pre-processing step. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).",
keywords = "Brain MRI, Classifier, Feature selection, Genetic algorithm, Machine learning, Supervised learning, Support vector machine",
author = "Tajik, {M. Nouman} and Rehman, {Atiq Ur} and Waleed Khan and Baber Khan",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-41009-8_25",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "233--244",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}