利用卷积神经网络奇异值分解提高脑肿瘤分割精度

P. Ahadian, Maryam Babaei, K. Parand
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引用次数: 0

摘要

脑瘤由表现出大脑异常生长的细胞组成。脑肿瘤的面积显著影响治疗类型的选择和治疗期间的病程。与此同时,脑核磁共振成像的图像伴随着噪音。消除已有噪声对更好地分割和诊断脑肿瘤有重要影响。在这项工作中,我们尝试使用特征值分析。我们使用MSVD算法,降低图像噪声,然后使用深度神经网络对图像中的肿瘤进行分割。与使用原始图像相比,该方法的准确率提高了2.4%。使用MSVD方法后,收敛速度也有所提高,表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy
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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