Development of a Novel Brain Tumor Classification Methodology Using Modified Deep Learning Principles
Pushpa Mohan, G. Ramkumar
Abstract
Currently, tumours rank as the second most common cancer kind. A great number of people are at risk because of cancer. In order to diagnose tumours like brain tumours, the medical sector requires a method that is quick, automated, efficient, and dependable. Treatment relies heavily on detection. Medical professionals will keep a patient safe if a tumour can be detected accurately. There are a number of image processing methods utilized by this programme. Many tumour patients have been saved because to this software, which allows physicians to give the right treatment. Unregulated cell growth is the hallmark of a tumour. As they multiply, brain tumour cells engulf all the nutrition that should be going to healthy brain cells and tissues, leading to brain failure. At present, doctors find out where the cancer is and how big it is by manually examining magnetic resonance pictures of the patient's brain. Not only is this an extremely time-consuming process, but it also leads to erroneous tumour detection. Modified Learning for Brain Tumour Classification (MLBTC) is a new tumour classification model that we presented in this paper. To evaluate its effectiveness, it is cross-validated using the current Convolutional Neural Network deep learning technology. Its purpose is to intelligently detect brain cancers. Using the most popular deep learning architectures, the suggested approaches intelligently classify brain tumours. This study aims to evaluate and analyze deep learning technologies with the purpose of guiding academics and medical professionals towards strong systems that identify brain tumours.