MIRAU-Net: An improved neural network based on U-Net for gliomas segmentation

Gliomas are the largest prevalent and destructive of brain tumors and have crucial parts for the diagnosing and treating of MRI brain tumors during segmentation using computerized methods. Recently, U-Net architecture has achieved impressive brain tumor segmentation, but this role remains challenging due to the differing severity and appearance of gliomas. Therefore, we proposed a novel encoder–decoder architecture called Multi Inception Residual Attention U-Net (MIRAU-Net) in this work. It integrates residual inception modules with attention gates into U-Net to further enhance brain tumor segmentation performance. Encoder–decoder is connected in this architecture through Inception Residual pathways to decrease the distance between their maps of features. We use the weight cross-entropy and generalized Dice (GDL) with focal Tversky loss functions to resolve the class imbalance problem. The evaluation performance of MIRAU-Net checked with Brats 2019 and obtained mean dice similarities of 0.885 for the whole tumor, 0.879 for the core area, and 0.818 for the enhancement tumor. Experiment results reveal that the suggested MIRAU-Net beats its baselines and provides better efficiency than recent techniques for brain tumor segmentation.