Paper Contents
Abstract
Abstract :Brain tumors, characterized by irregular cell growth within the brain, pose considerable health risks. Timely detection is crucial for optimizing treatment effectiveness and improving overall patient survival rates. Magnetic Resonance Imaging (MRI), widely employed for diagnostics, furnishes intricate images delineating the brain's structure and operations. Nonetheless, the manual identification and segmentation of brain tumors from MRI images represent intricate tasks, necessitating the proficiency of highly skilled professionals. In recent years, research endeavors have predominantly concentrated on conventional machine learning approaches for diagnosing brain tumors. Nevertheless, a discernible paradigm shift is observed towards the exploration of deep learning techniques, driven by their heightened capabilities and enhanced accuracy. This review paper endeavors to expound upon the noteworthy achievements within the realm of brain tumor diagnosis processes.
Copyright
Copyright © 2024 Batta Nagasaikiran. This is an open access article distributed under the Creative Commons Attribution License.
