Deep learning model for multi classification of brain tumour

Abstract

Brain Tumor refers to the pathological growth of cells that deviate from normal development, newlineoccurring either within the brain or in the adjacent tissues. These tumors are broadly newlineclassified into two categories benign (non-cancerous) or malignant (cancerous). Brain tumors newlinemust be detected early in order to improve the patientand#39;s prognosis and course of therapy. newlineAccurate segmentation and categorization of brain tumors are essential to create specialist newlinetreatment techniques. To create specialized treatment approaches, brain tumors must be newlineproperly classified, segmented, and graded. newlineCurrent diagnostic techniques mainly rely on radiologistsand#39; manual interpretation, which can newlinecause delays and human error, particularly when malignancies are still in the early stages. newlineTraditional imaging technologies, while useful, often fail to provide precise localization and newlineclassification of tumors, especially when dealing with varying shapes, sizes, and textures. newlineThus, there is a need for a more automated, effective, and precise method for detecting, newlineclassifying and grading brain tumors. The need was for a solution that could process medical newlineimaging data quickly and reliably, while ensuring accessibility and reducing the dependency newlineon human expertise, ultimately aiding in timely diagnosis and improved patient outcomes. newline