Detection and Analysis of Disease from Brain MRI Image Using Machine Learning

Abstract

Now a day’s tumor is second leading cause of cancer. Due to cancer large no of patients are in danger. The medical field needs fast, automated, efficient and reliable technique to detect tumor like brain tumor. Detection plays very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Using this   application   doctors   provide   proper treatment and save a number of tumor patients. A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in a way that they eventually take up all the nutrients meant for the healthy cells and tissues, which results in brain failure. Currently, doctors locate the position and the area of   brain   tumor   by   looking   at   the   MR   Images   of   the brain of the patient manually. This results in inaccurate detection of the tumor and is considered very time consuming. A tumor is a mass of tissue it grows out of control. We can use a Deep Learning architectures CNN (Convolution   Neural   Network) generally known as NN (Neural Network) and VGG 16(visual geometry group) Transfer learning for detect the brain tumor. In this paper, the design and implementation of a tumor detection system using two CNN models is considered. Digital image processing and Deep Learning technologies enable us to develop an automatic system for the diagnosis/detection of various kind of diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data augmentation, features extraction and classification; all these steps are discussed in details in the above sections. To work on CNNs, powerful GPU based system are required to speed up the process, lot of processing is carried out and also lot of RAM is required to process the images for testing. CNNs have also some options such as optimization technique selection, Number of Epoch, Batch size, iteration and learning rate. These options are tuned to get the optimal results from the CNN model. Learning rate is used to update the weights and bias in training phase, learning rate changes the   weights.   One   Epoch   is   when   the model see all images in training, as the training data maybe of very big sizes, the data in each Epoch is divided into batch sizes. Every epoch has a training and test session, after each Epoch the weights are updated according to   the   learning rate, optimization algorithms   are used to update the learning of a CNN adaptively. When the best weights for training are computed, the model is said to be trained. All the experimental work is carried out in MATLAB simulation tool.