Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

中文翻译：

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基于深度卷积神经网络和SVM算法的脑MRI肿瘤分割智能诊断方法。

在当前提出的脑分割方法中，基于传统图像处理和机器学习的脑肿瘤分割方法还不够理想。因此，基于深度学习的脑分割方法被广泛使用。在基于深度学习的脑肿瘤分割方法中，卷积网络模型具有良好的脑分割效果。深度卷积网络模型在编码和解码过程中存在大量参数和大量信息丢失的问题。本文提出了一种深度卷积神经网络融合支持向量机算法（DCNN-F-SVM）。提出的脑肿瘤分割模型主要分为三个阶段。在第一阶段，训练深度卷积神经网络以学习从图像空间到肿瘤标记物空间的映射。在第二阶段，将从深度卷积神经网络训练中获得的预测标签与测试图像一起输入到集成支持向量机分类器中。在第三阶段，将深度卷积神经网络和集成支持向量机串联以训练深度分类器。在BraTS数据集和自制数据集上运行每个模型以分割脑肿瘤。分割结果表明，该模型的性能明显优于深度卷积神经网络和集成的SVM分类器。一个深度卷积神经网络和一个集成支持向量机串联在一起，以训练一个深度分类器。在BraTS数据集和自制数据集上运行每个模型以分割脑肿瘤。分割结果表明，该模型的性能明显优于深度卷积神经网络和集成的SVM分类器。一个深度卷积神经网络和一个集成支持向量机串联在一起，以训练一个深度分类器。在BraTS数据集和自制数据集上运行每个模型以分割脑肿瘤。分割结果表明，该模型的性能明显优于深度卷积神经网络和集成的SVM分类器。