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Abstract: Now a day’s Brain tumour is second leading reason behind cancer. because of cancer massive no of patients are in danger. The medical area wants quick, automated, economical and reliable technique to notice tumor like {brain neoplasm|braintumour|tumor|tumour|neoplasm}. Detection plays vital role in treatment. If correct detection of neoplasm is feasible then doctors keep a patient out of danger. numerous image process techniques are employed in this application. mistreatment this application doctors offer correct treatment and save variety of neoplasm patients. A neoplasm is nothing however excess cells growing in associate uncontrolled manner. {brain neoplasm|brain tumour|tumor|tumour|neoplasm} cells grow in an exceedingly method that they eventually take up all the nutrients meant for the healthy cells and tissues, which ends in brain failure. Currently, doctors find the position and therefore the space of neoplasm by gazing the MRI pictures of brain of the patient manually. This leads to inaccurate detection of the neoplasm and is considered terribly time intense. A neoplasm may be a mass of tissue it grows out of management. We can use a Deep Learning architectures CNN (Convolution Neural Network) usually refers to as NN (Neural Network) and VGG 16(Very Deep Convolutional Networks) Transfer learning for notice the brain tumor. The performance of model is predict image neoplasm is gift or not in image. If the neoplasm is gift it come affirmative otherwise come.