Brain tumor is the growth of abnormal cells in brain some of which may leads to cancer. The usual method to detect brain tumor is Magnetic Resonance Imaging(MRI) scans. From the MRI images information about the abnormal tissue growth in the brain is identified. In various research papers, the detection of brain tumor is done by applying Machine Learning and Deep Learning algorithms. When these algorithms are applied on the MRI images the prediction of brain tumor is done very fast and a higher accuracy helps in providing the treatment to the patients. These prediction also helps the radiologist in making quick decisions. In the proposed work, a self defined Convolution Neural Network (CNN) is applied in detecting the presence of brain tumor and their performance is analyzed.
a) With the growth of Artificial Intelligence, Deep learning models are used to diagnose the brain tumor by taking the images of magnetic resonance imaging. Magnetic Resonances Imaging (MRI) is a type of scanning method that uses strong magnetic fields and radio waves to produce detailed images of the inner body.
b) Pre-Processing of Image - Using the following steps such as 1) Filter: Mean 2) Filter: Median 3) Filter: Wiener 4) Filter: Hybrid 5) Filter: Modified Hybrid. Median 6) Filter: Morphology. Based De-noising.
c) Image Segmentation - 1) Threshold Segmentation 2) Segmentation Based on Morphology 3) Convolutional Neural Network Algorithm
d) Feature Extraction - 1) Detection of edge 2) Edge detection: ‘Prewitt’ 3) Edge detection: ‘Robert’ 4) Edge detection: ‘Sobel’ 5) Feature Extraction using Histogram of Oriented Gradient.