Huygens Principle Imaging Method Powered by Deep Learning for Brain Stroke Classification

In our study, we investigate the potential of deep learning to elevate the effectiveness of the Huygens principle (HP) imaging method in detecting brain strokes. While HP has proven successful in breast cancer imaging, it faces some challenges when applied to more complex structures such as the brain. To overcome these challenges, we use Finite-Difference Time-Domain (FDTD) simulations to build a comprehensive dataset. Subsequently, we employ the U-net model to map HP-generated images to strokes, yielding impressive results. Our findings indicate an accuracy rate of 93.2% for the brain phantom, showcasing the promising prospect of this approach for brain imaging enhancement.