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@article{166580,
        author = {Khushi Shinde and Durva Detke},
        title = {A Review of Identification & Classification of Brain Tumor},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {2768-2773},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166580},
        abstract = {A new approach is presented in this paper for automatically detecting brain tumors using image processing. The uncontrolled and rapid growth of brain cells leads to the formation of brain tumors, which can be very dangerous if not treated promptly. Despite considerable efforts and promising outcomes, the accurate delineation and categorization of brain tumors remain complex due to variations in their location, structure, and size. The objective of this study is to offer researchers a comprehensive comprehension of the application of Magnetic Resonance Imaging (MRI) in the detection of brain tumors. The paper proposes various methods for detecting brain cancer using computational intelligence along with statistical image processing techniques. Furthermore, it encompasses an evaluation framework for specific systems using varied datasets. Furthermore, the research covers the shape and structure of brain tumors, existing data collections, methods for enhancing the datasets and isolating specific elements. It also evaluates different methods for categorizing.},
        keywords = {Brain tumor, MRI, computational intelligence, statistical image processing, segmentation, classification, Deep Learning (DL), Transfer Learning (TL), Machine Learning (ML), tumor detection, tumor morphology, dataset augmentation, component extraction, assessment matrix, medical imaging, artificial intelligence in healthcare.},
        month = {September},
        }