Study of Deep Learning-Based Segmentation and Classification of Brain Tumors in MRI Images

Brain tumors are one of the most progressive diseases affecting both children and adults. Brain tumors spread quickly and, if not treated properly, limit the patient’s chances of survival. It is important to detect malignant brain tumors as early as possible. Proper treatment planning and correct diagnosis are very important to prolong the life of the patient. The most precise method for identifying brain tumors is via magnetic resonance imaging (MRI). Finding brain tumors can be difficult because tumors vary in location, shape, and size. This study describes an MRI-based brain tumor segmentation method. To detect brain tumors, we can use architectures of that combines Convolution Neural Network (CNN), also known as Neural Network (NN), with visual geometry group (VGG 16) transfer learning to identify brain cancers. This study includes a literature analysis on deep learning models in order to discriminate between binary (normal and pathological) and multi-class (meningioma, glioma, and pituitary) brain cancers.