Early Brain Tumour Prediction using an Enhancement Feature Extraction Technique and Deep Neural Networks
S. Somasundaram1, R. Gobinath2

1Mr. S. Somasundaram, research Scholar Department of ComputerScience, VISTAS,Chennai, Tamil Nadu, India.

2Dr. R. Gobinath  Assistant Professor, Department of Computer Science, VISTAS,Chennai, Tamil Nadu, India.

Manuscript received on 02 October 2019 | Revised Manuscript received on 13 October 2019 | Manuscript Published on 29 June 2020 | PP: 170-174 | Volume-8 Issue-10S2 August 2019 | Retrieval Number: J103108810S19/2019©BEIESP | DOI: 10.35940/ijitee.J1031.08810S19

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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: Early tumor detection in the brain plays a vital role in early tumor diagnosis and radiotherapy planning. Magnetic resonance imaging (MRI) is latest technique which normally used for assessment of the brain tumor in Hospitals or scan centers. MRI images are used as the input image for brain tumor detection and classification. For predicting brain tumor earlier, an enhancement feature extraction technique and deep neural network are proposed. At first, the MRI image is pre-processed, segmented and feature extracted using image processing techniques. Support Vector Machine (SVM) based brain tumor classifications were performed previously with less accuracy rate. By using DNN classifier, there will be an improvement in accuracy rate. The proposed method mainly focuses on six features that are entropy, mean, correlation, contrast, energy and homogeneity. The performance metrics accuracy, sensitivity, and specificity are calculated to show that the proposed method is better compared to existing methods. The proposed technique is used to detect the location and the size of a tumor in the brain through MRI image by using MATLAB. 

Keywords: Magnetic Resonance Imaging; Deep Neural Network; Deep Learning NN Classifier; Performance Evaluation.
Scope of the Article: Neural Information Processing