A Scientific Approach to Building an Image Classification model of brain MRI images for Brain Tumor detection

The Computer associated-learning is one of the most significant achievements in field medical imaging. Generally, we use different technologies like computer tomography (CT scan) to diagnose disease or injury; lungs, liver, brain, etc. For this research, have used MRI image datasets for identification Brain tumor classification and non-tumor classification. Tumors are generally abnormal growth; if type growth occurs brain called a tumor. Early detection proper treatment may reduce chances cancer. vision domain where features extraction can be done very efficiently. In automatic types data sets considered using CNN (convolution neural network), i.e., VGG 16 architecture. With help pre-trained model classifying detecting tumors an existing set done. After augmentation volume improved individual values each class also found. Experimental result shows that accuracy 97 % online seen.