Advances in Feature Extraction for Brain Cancer Analysis: A Review of Traditional, Machine Learning, and Deep Learning Approaches

Brain cancer remains a formidable health challenge, necessitating continuous advancement in diagnostic and therapeutic strategies. Feature extraction techniques, encompassing traditional, machine learning, and deep learning-based methods, play a pivotal role in the early detection, prognosis, and treatment planning of brain tumors. This paper comprehensively analyses the state-of-the-art feature extraction methods employed in brain cancer research. The paper critically evaluates the strengths and weaknesses of various feature extraction approaches, including traditional handcrafted features, machine learning-based techniques, and advanced deep learning methods. Furthermore, we discuss the emerging trends and potential future directions in this dynamic field. By synthesizing the current literature, this review aims to provide researchers and clinicians with a valuable resource for understanding the role of feature extraction, spanning traditional, machine learning, and deep learning-based methods, in brain cancer analysis and promoting further advancements in this critical area of study.