Brain Tumor Detection using DNN Algorithm

Abstract

Machine Learning has provided the way for scientists to achieve great technical feats. An algorithm that achieves considerable results in detection of image and segmentation is known as Deep Neural Network. In a venture to augment and sharpen these techniques, we propose a DNN structure which utilizes stacked auto-encoders. Biopsies are used to classify brain tumors and are not normally performed prior to conclusive brain surgery. The advancement of machine learning will help in assisting the radiologists in tumor diagnostics without the use of invasive procedures. We leverage the speed and human-centric benefits of it to improve medical imaging facilities. With improved training speeds and accuracy, machine learning can open new doors for medical workers. It will simplify the process of understanding the human brain and will save a massive time bypassing the computational burden of scanning through medical images manually.