Brain Tumor Segmentation Using Modified Double U-Net Architecture

Abstract

Children and the elderly are most susceptible to brain tumors. It's deadly cancer caused by uncontrollable brain cell proliferation inside the skull. The heterogeneity of tumor cells makes classification extremely difficult. Image segmentation has been revolutionized because of the Convolution Neural Network (CNN), which is especially useful for medical images. Not only does the U-Net succeed in segmenting a wide range of medical pictures in general, but also in some particularly difficult instances. However, we uncovered severe problems in the standard models that have been used for medical image segmentation. As a result, we applied modification and created an efficient U-net-based deep learning architecture, which was examined on the Brain Tumor dataset from the Kaggle repository, which consists of over 1500 images of brain tumors together with their ground truth. After comparing our model to comparable cutting-edge approaches, we determined that our design resulted in at least a 10% improvement, showing that it generates more efficient, better, and robust results.