A Comprehensive Review Of Brain Tumor Segmentation Methods: Traditional Approaches, Deep Learning, And Hybrid Model

Authors: Pratik Pandey, Nagendra Patel

Abstract: Brain tumor segmentation plays a vital role in medical imaging by enabling accurate diagnosis, treatment planning, and monitoring of disease progression. Over the years, researchers have developed a wide range of segmentation techniques, each with its strengths and limitations. Traditional methods, such as thresholding, edge detection, and region-based techniques, offered simplicity and efficiency but often struggled with noise, variability, and ill-defined tumor boundaries. Statistical and model-based approaches, including clustering and deformable models, provided improved adaptability but required careful parameter tuning and high computational effort. The advent of machine learning, and more recently deep learning, particularly Convolutional Neural Networks (CNNs) and U-Net variants, has revolutionized segmentation, delivering unprecedented accuracy and robustness across diverse datasets. Hybrid approaches that integrate classical and deep learning methods are emerging as powerful solutions, balancing precision, efficiency, and generalizability. This review synthesizes these advancements, highlighting their evolution, comparative performance, and potential future directions.