A SURVEY OF BRAIN TUMOR DETECTION USING MACHINE LEARNING TECHNIQUES

Abstract

Brain tumors are one of the most critical health concerns worldwide, requiring accurate and timely diagnosis for effective treatment planning. The advent of machine learning techniques has shown promising results in automating the detection of brain tumors, aiding medical professionals in making informed decisions. This paper presents a comprehensive review of the application of machine learning approaches for brain tumor detection. The review encompasses various machine learning algorithms and methodologies employed in brain tumor detection and the challenges associated with brain tumor detection. The review concludes by summarizing the current state-of-the-art in machine learning-based brain tumor detection, identifying potential areas for improvement, and discussing future research directions. In conclusion, machine learning approaches have demonstrated significant potential in automating brain tumor detection, offering a valuable tool for radiologists and clinicians. However, further advancements and collaborations between machine learning experts and medical professionals are crucial to develop hybrid techniques and reliable models that can assist in early and accurate brain tumor diagnosis.