Inception residual network for brain tumor segmentation

Abstract

Brain tumors are a pathological condition characterized by aberrant growth within the cerebral structure. The segmentation of these tumors becomes pronounced to discern their boundaries accurately amidst healthy brain tissues, owing to the variability in tumor shapes and the complexities of determining their location, size, and texture. Manual tumor segmentation, a time-consuming task, is highly susceptible to human error. In this paper, we propose a deep inception residual network for brain tumor segmentation using the UNet architecture with a pre-trained Inception ResNet V2 encoder. The Inception-Resnet block, which fuses Inception with the residual neural network, is included. With this architecture, segmentation is significantly more robust. The study is being carried out on the RSNA-MICCAI Brain Tumor Radiogenomic Classification data set, i.e., on the benchmark dataset BraTS 2020. Our network achieves dice scores of 85.7%, 91.2%, and 83.2% for enhancing tumor, whole tumor, and tumor core, respectively. The experimental findings demonstrate the effectiveness of our proposed approach compared to other methods.