Residual Edge Attention in U-Net for Brain Tumour Segmentation

Abstract

Identification and delineation of the tumour area in images of the brain constitute the cnicial job of brain tumour segmentation in medical imaging. Tins task is crucial for diagnosis, treatment organizing, and keeping a track of brain nunours. Medical imaging methods like magnetic resonance imaging (MRI) or computed tomography scans are frequently used to divide brain tumours in real time. (CT). These imaging techniques provides high-resolution images for the brain that allows doctor to identify and locate nunours. There are several approaches to brain tumour segmentation, including manual segmentation by a radiologist, semi-automated segmentation using software tools that require some manual intervention, and fully automated segmentation using artificial intelligence (All algorithms. In this probing work. For segmenting brain nunours. we had anticipated Residual Edge Attention in U-Net design (ResEA-U-Net). Residual Edge Attention (ResEAl is a novel approach that enhances the performance of the U-Net architecture for brain rumour segmentation. The U-Net is often used in deep learning architecmre for medical MRI brain image segmentation tasks, but it suffers from limited receptive field and feature reuse. To address this limitation, ResEA is expected to capture wide-range dependencies and enable network to focus on important regions of the image. The ResEA block contains of a residual block and an attention block that are connected in series. The residual block is created to improve the gradient flow and feature reuse, while the attention block focuses on important regions of the image by assigning higher weights to informative edges. The expected approach to evaluated on the BraTS data, which contain images of magnetic resonance of brain nunours. Experimental outcomes demonstrate that the ResEA-U-Net outperforms the baseline U-Net and other state-of-the-art methods. Overall, the suggested ResEA-U-Net architecmre is a promising approach for brain nunour segmentation because it improves segmentation accuracy and lowers segmentation false positive rate, which can be essential for precise detection and therapy planning.