The document discusses using singular value decomposition (SVD) to reduce noise in MRI images before using a convolutional neural network (CNN) for brain tumor segmentation. SVD is applied using multiresolution SVD (MSVD) to decompose images into sub-bands and remove noise from high-frequency sub-bands. A U-Net CNN is then used to segment tumors. Results found MSVD improved segmentation accuracy by 2.4% over original images and increased CNN convergence speed. The proposed method effectively combined MSVD denoising with CNN segmentation for improved and faster brain tumor detection.