Published Paper Details:

In recent days, Magnetic Resonance Imaging (MRI) has played a major role in the field of medical imaging. Segmentation of MRI datasets helps in brain abnormalities detection by improved understanding, predicting the growth rate and enhanced treatment planning. Manual segmentation is identified to be more time consuming and sometimes the results are inaccurate. Automating the above task lies the real challenge owing to the problem of intensity inhomogeneity that occurs in the MRI dataset. Deep learning has been already excelling in wide ranged fields like text processing, Image Segmentation, Speech recognition, drug discovery, toxicology and various other fields. Deep learning for medical image analysis helps to integrate various ideas through their convolutional and fully connected networks. In this study we discuss about brain tumor which has its mortality level increasing at an alarming rate. In our proposal various brain tumor prediction and segmentation techniques are discussed and the best approach is compared for overall performance using various deep learning approaches.