Using Singular Value Decomposition in a Convolutional Neural Network to
  Improve Brain Tumor Segmentation Accuracy
Pegah Ahadian Maryam Babaei Kourosh Parand
A brain tumor consists of cells showing abnormal brain growth. The area of
the brain tumor significantly affects choosing the type of treatment and
following the course of the disease during the treatment. At the same time,
pictures of Brain MRIs are accompanied by noise. Eliminating existing noises
can significantly impact the better segmentation and diagnosis of brain tumors.
In this work, we have tried using the analysis of eigenvalues. We have used the
MSVD algorithm, reducing the image noise and then using the deep neural network
to segment the tumor in the images. The proposed method's accuracy was
increased by 2.4% compared to using the original images. With Using the MSVD
method, convergence speed has also increased, showing the proposed method's
effectiveness