AUTOMATED BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK IN MR IMAGES

Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors. The large amount of data produced by MRI prevents manual segmentation in a reasonable time. Automatic and reliable segmentation methods are required. Independent projection-based classification (LIPC) is used to segment the tumor region. Here, also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Here, proposed an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against over fitting, given the fewer number of weights in the network. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state-of-the-art brain tumor segmentation methods while being computationally efficient..