Brain Tumor Segmentation using CNN and DNN in MRI Images

Brain tumor extraction and its analysis are challenging tasks in Medical image processing because brain image is complicated. Segmentation plays a very important role in the medical image processing .Image segmentation is used to take out the suspicious parts from MRI. In that way MRI (magnetic resonance imaging) has become a useful medical diagnostic tool for the diagnosis of brain. In this project, segmentation using convolutional neural network method is implemented for tumor detection. The goal of segmentation is to simplify the representation of an image into something that is meaningful. In this project an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 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 also investigated the use of intensity normalization as a preprocessing 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. . In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs) and CNN. DNN while being extremely efficient. Here, give a description of different model choices that found to be necessary for obtaining competitive performance and explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data.