Improved brain tumour segmentation using modified U-Net model with inception and attention modules on multimodal MRI images

Abstract

ABSTRACT Brain tumours are currently recognised as one of the most dangerous diseases worldwide. Manual segmentation of brain tumours poses a challenging task heavily reliant on individual expertise, highlighting the need for the development of fully automated approaches. Deep learning techniques have gained significant popularity in recent years for medical image segmentation due to their exceptional accuracy and efficiency. In this study, we present a new brain tumour segmentation model based on the U-Net architecture. Our model incorporates an innovative inception module and a multi-level attention gate, effectively enhancing local feature expression and achieving superior segmentation accuracy. Additionally, we integrated a multiscale prediction fusion block to leverage global information across multiple scales. The performance of our proposed model was evaluated using the BRATS 2020 and BRATS 2018 datasets. On the BRATS 2018 dataset, our system achieved dice scores of 0.899, 0.851, and 0.838 for the whole tumour, tumour core, and enhancing tumour regions, respectively. Similarly, on the BRATS 2020 dataset, we obtained dice scores of 0.907, 0.842, and 0.829, respectively. Our proposed system demonstrated competitive performance across all brain tumour regions, as shown by the comparative analysis against state-of-the-art CNN models.