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<strong>Nowadays, Biomedical technology plays a vital role in diagnosis and treatment of small to dangerous life threatening diseases and one of the most life threatening disease is Brain Tumor, which is the mass growth of abnormal cells in brain. Early detection and treatment of it can save the human life by preventing the further growth of abnormal cells. Detection of it can be done by analysing the Magnetic Resonance Imaging (MRI) Scans. Accurate analysis of MRI Scans need to be done to detect the brain tumor and it can be achieved by using the algorithms of artificial neural networks, although human can detect manually but possibility to human errors is more and is time consuming. This paper proposes an effective algorithm model to predict brain tumor probability by using convolution neural networks. The algorithm includes image pre-processing in which noise is reduced using Gaussian filter and morphological operations. After that, images are normalized to scale fit. Batch normalization is added to the network to speed up the training. BRATS and Kaggle image dataset are used to train and evaluate the model to get maximised accuracy. Confusion matrix is used to evaluate the performance of the maximised model.</strong>

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