Using Singular Value Decomposition in a Convolutional Neural Network to
Improve Brain Tumor Segmentation Accuracy
- URL: http://arxiv.org/abs/2401.02537v1
- Date: Thu, 4 Jan 2024 20:57:25 GMT
- Title: Using Singular Value Decomposition in a Convolutional Neural Network to
Improve Brain Tumor Segmentation Accuracy
- Authors: Pegah Ahadian, Maryam Babaei, Kourosh Parand
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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
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