Review on Brain Tumor Segmentation and Classification using Artificial Intelligence

Every aspect of life has been altered for the better by the advancements in computer vision and Artificial Intelligence (AI) over the past decade. As a branch of AI, Machine Learning (ML) and Deep Learning (DL) have demonstrated impressive achievements across all sectors, particularly the biomedical field, thanks to their capacity to process massive amounts of data. Its potential has been employed in the identification of Brain Tumor (BT) using the medical image for efficient prognosis, with outstanding results. The primary goal of this study is to provide a comprehensive critical examination of previous research on the segmentation and classification of BT from medical images. Researchers that have extensive experience with AI and would like to apply their knowledge to the identification and classification of BT may find this analysis very useful. The literature on the application of AI in the segmentation and categorization of BT is briefly reviewed. This work focused on BT, covering topics such as BT anatomy, freely available data resources, enhancement methods, segmentation, feature extraction, and categorization techniques. From the investigation, it is clear that if there is a large amount of data, the DL model will perform better than the ML model. The data presented in this work will benefit future researchers with a comprehensive evaluation of current publications, as well as the performance of various AI methodologies. We are optimistic that this study will significantly advance BT research.