Open AccessExploring the Extreme Learning Machine for Classification of Brain MRIs
Author(s) -
Pranati Satapathy,
Sarbeswara Hota,
Sateesh Kumar Pradhan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1909.129219
Subject(s) - extreme learning machine , artificial intelligence , pattern recognition (psychology) , computer science , principal component analysis , discrete wavelet transform , feature (linguistics) , wavelet , feature extraction , wavelet transform , artificial neural network , linguistics , philosophy
Magnetic Resonance Imaging (MRI) technique of brain is the most important aspect of diagnosis of brain diseases. The manual analysis of MR images and identifying the brain diseases is tedious and error prone task for the radiologists and physicians. In this paper 2-Dimensional Discrete Wavelet Transformation (2D DWT) is used for feature extraction and Principal Component Analysis (PCA) is used for feature reduction. The three types of brain diseases i.e. Alzheimer, Glioma and Multiple Sclerosis are considered for this work. The Two Hidden layer Extreme learning Machine (TELM) is used for classification of samples into normal or pathological. The performance of the TELM is compared with basic ELM and the simulation results indicate that TELM outperformed the basic ELM method. Accuracy, Recall, Sensitivity and F-score are considered as the classification performance measures in this paper.