Brain Tumor Detection: A Comprehensive Review
This study provides a detailed analysis of the methodologies and techniques used in brain tumor analysis, which includes conventional machine learning (ML), convolutional neural networks (CNNs), capsule networks (CapsNets), and vision transformers (ViTs).
Abstract
Brain tumor classification and segmentation are crucial medical imaging procedures that aid in diagnosis and treatment planning. This study provides a detailed analysis of the methodologies and techniques used in this subject, which includes conventional machine learning (ML), convolutional neural networks (CNNs), capsule networks (CapsNets), and vision transformers (ViTs). This survey identifies key challenges and opportunities in brain tumor analysis and delineates future research directions. Extant literature predominantly focuses on individual tasks such as tumor detection, segmentation, or grade estimation, indicating the necessity for integrated frameworks capable of simultaneous multi-tasking, particularly with multi-grade classification. While CNNs have been extensively utilized, alternative architectures such as CapsNets and ViTs offer promising avenues for enhancing diagnostic accuracy and efficiency.