Design and Analysis for Advancements in Brain Tumor Detection Model by using Machine Learning Techniques

Brain tumors pose a significant threat due to their uncontrolled cell proliferation, necessitating timely and precise treatment. Despite notable advancements, accurate segmentation and classification of brain tumors remain challenging, given the complexity of tumor characteristics such as location, shape, and size. This research study explores into the complexities of brain tumor detection using Magnetic Resonance Imaging (MRI), including discussions on tumor shape analysis, data repositories, image enhancement techniques, segmentation methodologies, feature extraction, and classification algorithms. Furthermore, this study explores the emerging field of Machine Learning (ML), including deep learning, transfer learning, and quantum machine learning, within the field of brain tumor research. By considering relevant scholarly works, this research study interprets the benefits, limitations, recent advancements, and potential opportunities for brain tumor detection. Achieving sufficient tumor segmentation accuracy, reducing false positives, and accurately categorizing tumors into benign and malignant classes holds the potential to improve patient care and treatment outcomes.