Integrating CNNs, U-Net, and Transformers for Brain Tissue MRI Identification

Abstract

When it comes to neurological diagnosis, disease monitoring and treatment planning, you cannot overstate the importance of precisely identifying brain tissue components in an MRI. Yet most deep learning approaches in use today are hamstrung by their single-model design; they simply do not have the capacity to pick up on the long-range contextual dependencies and fine local structural nuances present in complex brain MRI data. To get around this, we have put forward an integrated hybrid framework that brings together CNNs, U-Net and Transformer modules. By drawing on the complementary strengths of each, our system is able to identify brain tissue with greater robustness. We put the model through its paces on standard benchmark segmentation datasets and saw steady gains compared to what you would expect from a conventional U-Net or CNN baseline. In terms of the Dice similarity coefficient we recorded an uptick of 4 to 6 per cent, and there was better delineation of boundaries for gray and white matter as well as cerebrospinal fluid, all without any loss in generalization or convergence stability. Ultimately, by marrying global contextual modeling with local feature extraction, this method makes for a more accurate and dependable tool for the automated MRI analysis needed in both research and the clinic