Classification of medical Images using metaheuristic feature selection methods
Maddula, Kuladeep Anand Kumar
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Magnetic Resonance Imaging (MRI) is a popular non-invasive diagnostic tool for brain imaging. Accurate analysis of brain MRI images help in early detection of brain tumors and could save lot of lives. But accurate classification of the images as normal or pathological is a challenging task from the clinical as well as technology stand point. Brain MRI images consists of a large information set which contain redundancy in determining the condition of the brain. The redundant information would lead to increase in dimensionality of the data. Therefore, using a feature selection algorithm to find an optimum set of features would reduce the time and computation complexity of the classifiers for distinguishing the brain MRI images. This work is to study the performance of feature selection with different meta-heuristic search algorithms with multiple fitness functions. The three meta-heuristic algorithms considered are Binary Genetic Algorithm, Binary Particle Swarm Optimization and Binary Grey Wolf Optimizer for selecting an optimal set of features out of the extracted features from brain MRI images. The feature selection is performed on the 13 statistical features extracted from the brain MRI images using Discrete Wavelet Transform, Principle Component Analysis and Grey Level Co-occurrence matrix. The performance of the feature selection algorithms are compared by applying 4 different sets of features from each algorithm to seven different test classifiers. Our results obtained show high performance using feature selection.