TIDBITS is a machine-learning based model that makes use of CNN algorithm for detection of tumors in the MRI scans of the human brain. This brain tumor detection system helps in aiding towards improved accuracy with a coRepository
The human body is composed of several organs, each responsible for different functions. The action of these organs are governed by the most vital and critical organ known as 'brain'. The body cannot perform a single task without the signals being transmitted through the brain. Thus, it depends on the healthy state of this most important organ. There can be a situation where the brain stops functioning properly. One of the common reasons for this dysfunction is 'brain tumor'. A tumor is a collection of abnormal cells in the brain as a result of excess growth in an uncontrolled manner. Such alien cells consume the nutrients meant for the healthy cells leading to their death. This is a form of cancer and ultimately causes brain failure. The cancer incidence rate is growing at an alarming rate in the world. Brain Tumor is a major cause of death and responsible for around 11% of all deaths worldwide. Therefore, detection of brain tumors is very important in its earliest stage. Currently, many doctors locate the position and the area of brain tumor by looking at the MR Images of the brain of the patient manually. This may result in inaccurate detection of the tumor and is considered very time consuming. Diagnosing brain cancer begins with taking a thorough personal and family medical history, including symptoms and risk factors for brain cancer. The diagnostic process also includes completing a thorough physical and neurological exam. This project deals with a system that uses computer-based procedures to detect tumor blocks in the brain using Convolution Neural Network Algorithm for MRI images of different patients. Different types of image processing techniques like image segmentation, image enhancement and feature extraction are used for the brain tumor detection in the MRI images of the cancer-affected patients. Detection of brain tumor is done by using the following techniques: Image pre-processing, Image segmentation, Feature extraction, and Classification.
I have built this project using OpenCV coupled with Python language. OpenCV has many built-in libraries and functions for the processing of images. The extensive libraries available in Python helps in making the project even more effective and faster to implement.
Project created by Juhi Pathak