Brain Tumor Detection Using Advanced Deep Learning Implementations

Abstract

Modern technological advancements are concentrated on the development of intelligent machines or software that mimic and respond like humans. Today's Artificial Intelligence computing activities encompass language processing, perception, learning, planning, and problem-solving. Early cancer detection is essential to saving as many lives as possible. A recent report from the "World Health Organization" (WHO) in February 2018 highlighted mortality associated with brain tumors or the "CNS" (Central Nervous System). This paper primarily aims to detect and predict the presence of brain tumors in individuals using "MRI" (Magnetic Resonance Imaging) brain scan images. This is achieved through machine learning techniques in classification. A model for identifying brain tumors is created using a deep learning algorithm and a dataset comprising thousands of images. "Convolutional Neural Networks" (CNNs) are employed to identify and predict the likelihood of the presence of a brain tumor in an individual, based on the provided MRI scan image. This work explores several potential mechanisms for using deep learning techniques to construct models for brain tumor detection. The objective is to discover more effective methods to detect brain tumors based on MRI scans, thereby enabling neurologists to make decisions with increased ease, accuracy, and speed. Manual classification of brain tumors using only MRI images can be time-consuming, potentially delaying necessary treatment for the affected individual. Therefore, the assistive use of machine learning technology can help healthcare professionals enhance their work in combating brain tumors, a severe medical condition.