Segmentation of Brain Tumor using Glcm and Discrete Wavelet Transform
Alpana Jijja1, Dinesh Rai2
1Alpana Jijja*, CSE, Ansal University, Gurugram, Haryana, India.
2Dr.Dinesh Rai, CSE, Ansal University, Gurugram, Haryana, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on April 10, 2020. | PP: 344-348 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3812049620/2020©BEIESP | DOI: 10.35940/ijitee.F3812.049620
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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: To identify brain tumors at an early stage is a challenging task. The brain tumor is usually diagnosed with Magnetic Resonance Imaging (MRI). When MRI spectacles a tumor in the brain, the most common way of determining the type of brain tumor after a biopsy or surgery is to look at the results of a tissue sample. In this research to detect brain tumors faster and accurately the feature extraction techniques are used to segment the tumor affected area. One of such very effective technique of feature extraction measure is the Grayscale Co-occurrence Matrix (GLCM). This research focuses on the GLCM and Discrete Wavelet Transformation (DWT) technique to detect and label the tumor from an image based on the textures and categorizing it according to a tumor or non-tumor category. The convolutional neural network (CNN) uses these features to improve the accuracy to 91%.
Keywords: Convolutional Neural Network, Discrete Wavelet Transform, Feature Extraction, Grayscale Co-occurrence Method.
Scope of the Article: Discrete Optimization