High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Brain Tumour Detection Using MRI Images


Shubhangi Vaman Jondhale , Sir Visvesvaraya Institute of Technology Nashik; Kanchan Damodhar Chavanke, Sir Visvesvaraya Institute of Technology Nashik; Ashwini Ravindra Shinde, Sir Visvesvaraya Institute of Technology Nashik; Vaishnavi Bhausaheb Aandhale, Sir Visvesvaraya Institute of Technology Nashik; Prof. Narayan B. Vikhe, Sir Visvesvaraya Institute of Technology Nashik


Brain Tumor, Magnetic Resonance Imaging, Adaptive Bilateral Filter, Convolution Neural Network


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. The human brain is the major controller of the humanoid system. The abnormal growth and division of cells in the brain lead to a brain tumor, and the further growth of brain tumors leads to brain cancer. In the area of human health, Computer Vision plays a significant role, which reduces the human judgment that gives accurate results. CT scans, X-Ray, and MRI scans are the common imaging methods among magnetic resonance imaging (MRI) that are the most reliable and secure. MRI detects every minute objects. Our paper aims to focus on the use of different techniques for the discovery of brain cancer using brain MRI. In this study, we performed pre-processing using the bilateral filter (BF) for removal of the noises that are present in an MR image. This was followed by the binary thresholding and Convolution Neural Network (CNN) segmentation techniques for reliable detection of the tumor region. Training, testing, and validation datasets are used. Based on our machine, we will predict whether the subject has a brain tumor or not. The resultant outcomes will be examined through various performance examined metrics that include accuracy, sensitivity, and specificity. It is desired that the proposed work would exhibit a more exceptional performance over its counter parts. Convolutional neural network (CNN) architecture was developed for learning the intricate patterns in the Magnetic Resonance Imaging (MRI) scans for the detection of Brain Tumor. Therefore, the above approaches can provide a solid solution for the detection of Brain Tumor in the preliminary or early stage prediction of the Brain Tumor and can be able to increase the lifespan of the diseased patient with proper treatments and medications leads to peaceful life.

Other Details

Paper ID: IJSRDV9I120074
Published in: Volume : 9, Issue : 12
Publication Date: 01/03/2022
Page(s): 122-127

Article Preview

Download Article