Big data analysis for brain tumor detection: Deep convolutional neural networks
Big data analysis for brain tumor detection: Deep convolutional neural networks
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DOI：
10.1016/j.future.2018.04.065
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发表时间：
2018-10-01
影响因子：
7.5
通讯作者：
Fernandes, Steven Lawrence
中科院分区：
文献类型：
--
作者：
Amin, Javeria;Sharif, Muhammad;Fernandes, Steven Lawrence
脑肿瘤检测是脑图像处理中的一个活跃研究领域。本文提出了一种利用磁共振图像对脑肿瘤进行分割和分类的方法。采用基于深度神经网络(DNN)的结构进行肿瘤分割。在所提出的模型中，07层用于分类，包括03卷积、03 REU和Softmax层。首先将输入的MR图像分成多个块，然后将每个块的中心像素值提供给DNN。DNN根据中心像素分配标签并进行分割。使用八个大规模基准数据集进行了广泛的实验，包括BRATS 2012(图像数据集和合成数据集)、2013(图像数据集和合成数据集)、2014、2015和ISLES(缺血性卒中病变分割)2015和2017。分别以准确度(ACC)、敏感度(SE)、特异度(SP)、骰子相似系数(DSC)、精密度、假阳性率(FPR)、真阳性率(TPR)和Jaccard相似指数(JSI)对结果进行验证。(C)2018年，由爱思唯尔出版。
Brain tumor detection is an active area of research in brain image processing. In this work, a methodology is proposed to segment and classify the brain tumor using magnetic resonance images (MRI). Deep Neural Networks (DNN) based architecture is employed for tumor segmentation. In the proposed model, 07 layers are used for classification that consist of 03 convolutional, 03 ReLU and a softmax layer. First the input MR image is divided into multiple patches and then the center pixel value of each patch is supplied to the DNN. DNN assign labels according to center pixels and perform segmentation. Extensive experiments are performed using eight large scale benchmark datasets including BRATS 2012 (image dataset and synthetic dataset), 2013 (image dataset and synthetic dataset), 2014, 2015 and ISLES (Ischemic stroke lesion segmentation) 2015 and 2017. The results are validated on accuracy (ACC), sensitivity (SE), specificity (SP), Dice Similarity Coefficient (DSC), precision, false positive rate (FPR), true positive rate (TPR) and Jaccard similarity index (JSI) respectively. (C) 2018 Published by Elsevier B.V.