EFFECTIVE CLASSIFICATION OF IMAGES USING ARTIFICIAL NEURAL NETWORK ON BRAIN TUMOR DETECTION

Classification of brain tumor is considered a significant diagnostic restriction for medical applications. In this article, we review the classification of the brain tumor in order to separate tumor from pituitary tumor, glioma tumor and meningioma tumour, by considering the restriction as classification issue. This approach uses the idea of deeper learning to draw brain characteristics from the images of MRI. The recurring neural network is used in this analysis to distinguish extracted brain characteristics. The tests are conducted in three times over the brain image data collection for the MRI cross-validation phase. The findings indicate that the proposed RNN categorisation efficiently classifies brain tumors with an average precision of 98 per cent over other current approaches