Exploring the Extreme Learning Machine for Classification of Brain MRIs
Pranati Satapathy1, Sarbeswara Hota2, Sateesh Kumar Pradhan3

1Pranati Satapathy, Dept. of Computer Science and Applications, Utkal University, Bhubaneswar, India.
2Sarbeswara Hota*, Computer Application Dept. Siksha O Anusandhan Deemed to be University, Bhubaneswar, India.
3Sateesh Kumar Pradhan, Computer Science and Applications, Utkal University, Bhubaneswar, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 3654-3657  | Volume-9 Issue-2, December, 2019. | Retrieval Number:  A1909109119/2019©BEIESP | DOI: 10.35940/ijeat.A1909.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Wavelet Transformation, Principal Component Analysis, Extreme Learning Machine, Magnetic Resonance Imaging.