Computer Vision Based Neuroimage Analysis using Extensive Feature Extraction and Selection Techniques for Brain Tumor Detection: A Review

Detection and diagnosis of Brain Tumors (BT) are of the utmost significance aspects in the process of preventative measures and therapeutic planning for patients. The procedure of assessing BT has become substantially more automated and legitimate throughout the time of the inception of machine learning (ML) and deep learning (DL) techniques. These innovations have brought about outstanding improvement. This paper provides a comprehensive review of the most recent research on brain tumor detection and diagnosis using computational techniques, with a focus on feature extraction and selection techniques. We review multiple approaches to feature extraction and selection, including conventional techniques and proficient DL architectures. In addition, we discuss the challenges and opportunities associated with these techniques, as well as possible future developments in this promptly evolving field.