Development of a Novel Neural Network Model for Brain Image Classification
Pranati Satapathy1, Sateesh Kumar Pradhan2, Sarbeswara Hota3

1Pranati Satapathy, Dept. of Computer Science and Applications, Utkal University, Bhubaneswar, India.
2Sateesh Kumar Pradhan, Computer Science and Applications, Utkal University, Bhubaneswar, India.
3Sarbeswara Hota, Computer Application, Siksha O Anusandhan Deemed to be University, Bhubaneswar, India. 

Manuscript received on 13 August 2019. | Revised Manuscript received on 19 August 2019. | Manuscript published on 30 September 2019. | PP: 7230-7235 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6262098319/2019©BEIESP | DOI: 10.35940/ijrte.C6262.098319
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Abstract: The analysis of brain MRI images is highly beneficial for the medical practitioners. Since the manual study of these images are time consuming and tedious, the automated process using software based system have been developed. The machine learning techniques are applied in developing brain MR image classification process. The classification process consists of dataset preparation, feature extraction, feature reduction and the use of classifier. In this paper, 2D DWT is used for feature extraction and PCA is used for feature reduction. ELM model is used as a classifier. The input weights and biases in ELM are randomly assigned. So EHO algorithm, a newly developed bio inspired algorithm is used to optimally determine the input weights and biases of ELM model. The classification performance of the EHO-ELM model is compared with basic ELM model for three of the brain MR image datasets. From the simulation study, it is found that the proposed EHO-ELM model outperformed the basic ELM model.
Keywords: Elephant Herding Optimization, Machine Learning, Magnetic Resonance Imaging, Principal Component Analysis, Sensitivity.

Scope of the Article: Classification