|Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm|
|T.Gopi Krishna1 , K.V.N. Sunitha2 , S. Mishra3|
1 Dept. of Computer Science and Engineering, Rayalaseema University, Kurnool, India.
2 Dept. of Computer Science and Engineering, BVRIT Women’s Engineering College, Hyderabad, India.
3 Electronics and Communication Engineering Program, Adama Science and Technology University, Adama, Ethiopia .
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Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-1 , Page no. 18-23, Jan-2018
Online published on Jan 31, 2018
Copyright © T.Gopi Krishna, K.V.N. Sunitha, S. Mishra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: T.Gopi Krishna, K.V.N. Sunitha, S. Mishra, “Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.18-23, 2018.
MLA Style Citation: T.Gopi Krishna, K.V.N. Sunitha, S. Mishra "Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm." International Journal of Computer Sciences and Engineering 6.1 (2018): 18-23.
APA Style Citation: T.Gopi Krishna, K.V.N. Sunitha, S. Mishra, (2018). Detection and Classification of Brain Tumor from MRI Medical Image using Wavelet Transform and PSO based LLRBFNN Algorithm. International Journal of Computer Sciences and Engineering, 6(1), 18-23.
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|It is a difficult and complex task for a radiologist or clinical practitioner to segment, detect, and extract infected tumor area and classify the type of tumor from magnetic resonance (MR) images. This paper presents a PSO (Particle Swarm Optimization) based LLRBFNN (Local Linear Radial Basis Function Neural Network) model to classify and detect brain tumors into malignant (cancerous) and benign (noncancerous). In this paper we have used wavelet transform to improve the performance of MR image segmentation process and feature extraction. For the validation of the proposed PSO based LLRBFNN model, the machine learning approach support vector machine (SVM) and LMS (Least Mean Square) based LLRBFNN classifier also investigated. The research work follows the steps such as feature extraction out of which relevant features are considered for the research work. In the second step the features are fed as input to the proposed PSO based LLRBFNN Model for the classification task. In the third step the machine learning approach SVM and LMS based LLRBFNN has been applied for classification task and the results are compared. It is found that the proposed model takes less computational time than the SVM and LMS based LLRBFNN machine learning approach. In contrast to classification results the proposed model gives better classification results. Based on accuracy it is also noticed that the proposed model shows better performance in accuracy and quality analysis on MRI brain images.|
|Key-Words / Index Term :|
|SVM, Wavelet Transform, DWT, PSO, LLRBFNN, Brain tumour, Feature extraction|
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