BRAIN TUMER CLASSIFICATION USING CNN FRAMEWORK
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Abstract
Brain tumour segmentation is considered a complex procedure in magnetic resonance imaging (MRI), given the diversity of tumour forms and the complexity of determining tumour location, size, and shape. Manual segmentation of tumours is a time-consuming task that is very sensitive to human error. Therefore, this study offers an automated approach that can detect tumour fragments and divide the tumour into all image slices in volume MRI brain scanners. First, a set of algorithms is used in the pre-processing phase to clean up and validate the collected data. Grey-Level Similarity Matrix Analysis and Analysis Of Variance (ANOVA) are used to obtain and select entities, respectively. Multilayer perceptron is accepted as a neural network classification, and a limited 3D window genetic algorithm is used to determine the location of abnormal tissue in MRI slices. Finally, active borderless 3D imaging is used to segment brain tumours using volume MRI exams. The experimental data set contains 165 patient images collected from the MRT unit of Al-Qadimiyah University Hospital in Iraq. Tumour resection results achieved 89% _4.7% accuracy compared to manual procedures.
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