Enhanced U-Net Architecture for Brain Tumour Localization and Segmentation in T1-Weighted MRI

Detection and segmentation of Magnetic Resonance Imaging (MRI) scans is a critical task in medical imaging, where achieving high segmentation precision and reliability remains challenging due to variations in tumor shape, size, intensity, and boundary definition across different MRI modalities. Recently, deep learning techniques have significantly improved the efficiency oflocalization and segmentation of various medical fields, including brain tumoursanalysis. This paper presents a novel two-stage approach for brain tumour segmentation in T1-weighted contrast-enhanced MRI (CE-MRI) scans, leveraging both YOLO(you only look once) and Modified U-Net. In the first stage, YOLO is employed to quickly and accurately localize regions of interest (ROIs) where brain tumours are present. To accelerate this step, YOLOv3 are incorporated, improving the computation speed and efficiency of the model. In the second stage, a modified U-NET model, enhanced with spatial and channel attention modules, is utilized to perform precise segmentation of the tumours within the identified ROIs. The performance of the proposed framework is evaluated using metrics including precision, recall, Jaccard index, and Dice similarity coefficient (DSC). Our approach demonstrates promising results compared to previous methods using the same database.