Abstract
In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.
Highlights
In such a modern day’s there is no expectation to live a good life without healthy body organs
The neural network mainly deals with human brain learning and it consists of a neuron that is responsible to create the layers in the Artificial Neural Network (ANN) model
A simple hybrid ANN model is proposed based on 2D-Discrete Wavelet Transformation (DWT) and ANN to classify benign and malignant tumors using MRI images
Summary
In such a modern day’s there is no expectation to live a good life without healthy body organs. Cell generation and dead cell replacement is a process that is controlled by the human body, if this process is failed due to some malfunctioning of the human body a large number of cells are generated. If these extra generated cells in the place of one cell gain some mass, called a tumor. The extra generated cells are covered by a membrane and removed by a small surgery. If these cells are diagnosed in an early stage, chances to suffer by the cancer of human being is reduced.
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