Evaluation of Data Sets and Algorithms for Brain Tumor Detection Using MRI Images: A Python-Based Approach

Abstract

This study is to evaluate the work of the data sets and major algorithms that are involved in the brain tumor detection system using the MRI image with the help of the python concept. So basically, is an easy manner if we require to define the phenomena of the brain tumor it could be the abnormal condition that causes the problem of cancer, here the abnormal condition refer to the growth in cell body in not a very suitable way for the brain tissue. In the respective paper, we proposed an algorithm to segment brain tumor from 2D Magnetic Resonance Image of the brain by a CNN. When this algorithm is applied to MRI images, a brain tumor diagnosis can be made more quickly and accurately, which makes it easier to give patients treatment. These predictions enable the radiologist to make quick decisions as well. In the proposed work, the performance of a self-defined Convolution Neural Network (CNN) is evaluated. For the purpose of faster and efficient accuracy, we will implement the proposed method using the “TenserFlow” and “keras” in “Python.