Deep Learning Algorithm for Brain Tumor Detection and Analysis Using MR Brain Images

Abd El Kader Isselmou, Guizhi Xu, Shuai Zhang, S. Saminu, Imran Javaid
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引用次数: 8

Abstract

Medical image processing paly a good role in helping the radiologists and facility patients diagnosis, the aims of this paper is created deep learning algorithm to detect brain tumor using magnetic resonance brain images and analysis the performance of algorithm based on different values, accuracy, sensitivity, specificity, ndice, nJaccard coeff and recall values. The significance of convolution neural network (CNN) it's the ability to detect brain clearly with high performance. We propose framework is successfully tested on data source on magnetic resonance brain images of the patients suffering from different brain tumor types reaching a Dice similarity 86,785% and high accuracy 98, 33%.
基于MR脑图像的脑肿瘤检测与分析的深度学习算法
医学图像处理在帮助放射科医生和设施患者诊断方面发挥了很好的作用,本文的目的是建立基于磁共振脑图像的深度学习算法来检测脑肿瘤,并基于不同的值、准确性、灵敏度、特异性、nice、nJaccard coeff和召回率分析算法的性能。卷积神经网络(convolutional neural network, CNN)的意义在于它能以高性能清晰地检测大脑。我们所提出的框架在不同类型脑肿瘤患者的脑磁共振图像数据源上进行了成功的测试,达到了86,785%的Dice相似度和98.33%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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