Brain Tumor Detection and Localization Using MRI Images

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 are using a Deep Learning architectures CNN (Convolution Neural Network) generally known as NN (Neural Network) and U-Net learning to detect the brain tumor. The performance of model is predict image tumor is present or not in image. If the tumor is present it return yes otherwise return no. According to recent analysis, lower-grade glioma tumors have been identified to possess distinct genomic subtypes that are correlated with shape features. The present study introduces a fully automated approach for quantifying tumor imaging characteristics through the utilization of deep learning-based segmentation. The study further investigates the potential of these characteristics in predicting tumor genomic subtypes. Preoperative imaging and genomic data of 110 patients diagnosed with lower-grade gliomas from The Cancer Genome Atlas were utilized in this study, which was conducted across five different institutions. Three features were extracted from automatic deep learning segmentations, which quantify both two-dimensional and three-dimensional characteristics of the tumors. In order to examine the correlation between imaging characteristics and genomic clusters, we performed a Fisher exact test on 10 hypotheses for every combination of imaging feature and genomic subtype.