Computational Intelligence approach to improve the Classification accuracy of Brain Tumor Detection

Abstract

One of the most dangerous diseases is a brain tumor that may affect children as well as adults both. Brain tumors are responsible for 80-90 percent of all primary Central Nervous System (CNS) cancers. Each year, around 12,000 A brain tumor is discovered in a person. When diagnosed with a malignant brain or CNS tumor, males and the five-year survival rate for women is approximately 33% and 37% respectively. There are several types of brain tumors, including pituitary tumors, malignant tumors, benign tumors and more. Increased life expectancy for patients should be achieved by appropriate therapy, planning, and precise diagnostics. Magnetic resonance imaging is the most effective method for identifying brain tumors (MRI). Scanners produce massive volumes of picture data. The radiologist examines these pictures. Automated classification technologies such as Artificial intelligence (AI) and machine learning (ML) have regularly outperformed manual categorization in terms of accuracy. As a consequence, offering a system that employs Deep Learning Techniques and Algorithms such as ANN (Artificial Neural Networks), CNN (Convolution Neural Networks), TL (Transfer Learning) and GLCM for recognizing and tracking it will benefit doctors worldwide.