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Pathological brain detection based on AlexNet and transfer learning

机译:基于AlexNet和转移学习的病理脑检测

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The aim of this study is to automatically detect pathological brain in magnetic resonance images (MRI) based on deep learning structure and transfer learning. Deep learning is now the hottest topic both in academia and industry. However, the volume of brain MRI datasets are usually too small to train the entire deep learning structure. The training can be easily trapped into overfitting. Therefore, we introduced transfer learning to train the deep neural network. Firstly, we obtained the pre-trained AlexNet structure. Then, we replaced parameters of the last three layers with random weights and the rest parameters served as the initial values. Finally, we trained the modified model with our MRI dataset. Experiment results suggested that our method achieved accuracy of 100.00%, which outperformed state-of-the-art approaches. Crown Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.
机译:这项研究的目的是基于深度学习结构和转移学习来自动检测磁共振图像(MRI)中的病理性大脑。深度学习现已成为学术界和行业中最热门的话题。但是,大脑MRI数据集的数量通常太小,无法训练整个深度学习结构。培训很容易陷入过度拟合中。因此,我们引入了转移学习来训练深度神经网络。首先,我们获得了预先训练的AlexNet结构。然后,我们将后三层的参数替换为随机权重,其余参数用作初始值。最后,我们使用MRI数据集训练了修改后的模型。实验结果表明,我们的方法达到了100.00%的精度,优于最新方法。官方版权(C)2018,由Elsevier B.V.保留所有权利。

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