A Study on Brain Tumor in Various Fields using Machine Learning

Brain tumors are the result of cells developing fast and irregularly. If it is not treated in the early stages, it could result in death. Precision classification and segmentation remain challenging jobs, despite numerous large initiatives and good outcomes in this discipline. Brain tumor detection is significantly hampered by the variations in tumor location, size, and shape. The identification of anomalies in the brain is a vital undertaking in the medical sciences. The anatomy of brain tumors, feature extraction, augmentation techniques, transfer learning, deep learning, public databases, quantum machine learning and classification of the research of brain tumors were all included in this survey. Medical image processing gives fundamental details on brain abnormalities and aids in the best possible therapy strategy for the patient. There are multiple processes in the image-processing methodology employed to detect brain tumors. Classification, segmentation, feature extraction, and pre-processing were the steps. Several attribute extraction strategies are explored in this survey. The classification and feature extraction methods utilized for image analysis for brain tumor diagnosis are properly reviewed in more than fifteen research articles. Additionally, this study provides all pertinent information for the diagnosis of brain tumors, along with its advantages, disadvantages, developments, and prospects.