Revolutionizing Brain Tumour Detection: Integrating 3D U-Net-R Segmentation with Volume Analysis for high Diagnostic Accuracy

Abstract

Brain tumour diagnosis in early stages is important for planning for the treatment in advance, patient prognosis and medical management. However, it is difficult for radiologists and medical practitioner to make an accurate diagnosis and plan. It interprets brain tumours from medical images, making the process time-consuming. The aim of this proposal is to better understand and assess the mechanism of 3D deep learning U-Net-R can help us detect precisely the brain tumours from medical images which has special feature of comprehensive understanding of the spatial context with in the data, preserving fine grained details and also the ability to demarcate the complex structures. Problems like merging multi-image data (3D) using instantaneous volume analysis. The scarcity of dis aggregated images and annotated data will be the primary focus and also perform a volume analysis to determine the correct sectional image and volume of the tumour can also be used in this research as a symbol is improved segmentation. The goal of this update is to target medical aid in the initial surgical staffing decision. 3D U-Net-R model which is combination of U-net architecture and residual learning has shown superiority performance compared to previous models, providing improved analytical accuracy and reliability.