International Journal For Multidisciplinary Research

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Methodologies and Challenges: Detection and Classification Techniques for Brain Tumor of Magnetic Resonance Images

Author(s) Raghawendra Sinha, Dipti Verma
Country India
Abstract Brain tumors are now the 10th most prevalent type of tumor, affecting both children and adults, thanks to a considerable rise in incidence in recent years. If caught early enough, It is also one of the tumor forms that is most easily treated. In order to detect the kind and stage of tumor, scientists and researchers have been attempting to create advanced procedures and approaches. For re-sectioning and assessing irregularities in the shape, size, or location of brain tissues that in turn aid in the detection of tumors, two techniques that are extensively utilized are Magnetic Resonance Imaging (MRI) and Computer Tomography (CT). Doctors favor MRI over CT scan because of its benefits, which are addressed later in the text. As MRI provides non-invasive imaging, the cerebrum is one of the most profoundly involved locations in the medical science network. This paper offers a thorough review of the literature on approaches for detecting brain tumors and classifying abnormalities and normalcy in MRI images based on many methodologies such as deep learning techniques, meta-heuristic techniques, and their hybridization. It consists of the presentation and quantitative investigation of best-in-class strategies using conventional detection and classification techniques.
Keywords Brain Tumor Classification, Medical Image Segmentation, Brain Tumor Detection, Magnetic Resonance Imaging, MRI
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 4, Issue 5, September-October 2022
Published On 2022-09-11
Cite This Methodologies and Challenges: Detection and Classification Techniques for Brain Tumor of Magnetic Resonance Images - Raghawendra Sinha, Dipti Verma - IJFMR Volume 4, Issue 5, September-October 2022. DOI 10.36948/ijfmr.2022.v04i05.026
DOI https://doi.org/10.36948/ijfmr.2022.v04i05.026
Short DOI https://doi.org/10/gqt6ww
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