Brain Tumor Detection Using Supervised Learning: A Survey

Abstract

With the advancement in technology, artificial intelligence and computer vision are being used extensively in health care sector. Specifically, there’s a lot of research happening in brain tumor detection and classification. A brain tumor can be defined as a chronic disease in which the brain tissues start to grow in an uncontrollable manner. There are very few technologies currently in use to detect brain tumors such as CT - Scans and MRIs. And, such technologies require expert diagnosis of the type and location of the tumor, and such tasks are time-consuming. This is the reason, there is a need for an automatic brain tumor detection system that can make the diagnosis faster. The survey paper will review the supervised machine learning algorithm and supervised neural network algorithms that can be employed to detect the tumor in 2D brain images. The experiments were carried out using SVM and other deep neural network approaches like ANN, CNN, VGG-16, ResNet, and InceptionNet. The dataset was downloaded from Kaggle. The average testing accuracy achieved was 97.76%