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. 2020 Jul 14:2020:6789306.
doi: 10.1155/2020/6789306. eCollection 2020.

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

Wentao Wu  1 , Daning Li  2 , Jiaoyang Du  1 , Xiangyu Gao  2 , Wen Gu  3 , Fanfan Zhao  2 , Xiaojie Feng  2 , Hong Yan  1
Affiliations

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

Wentao Wu et al. Comput Math Methods Med. .

Abstract

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.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
MRI of glioma: (a) T1-weighted, (b) postcontrast T1-weighted, (c) T2-weighted, and (d) FLAIR.
Figure 2
Figure 2
Flow chart of glioma segmentation algorithm based on deep learning.
Figure 3
Figure 3
LeNet convolutional neural network structure.
Figure 4
Figure 4
CNN flow chart.
Figure 5
Figure 5
Tumor area division of glioma.
Figure 6
Figure 6
The proposed model flow chart.
Figure 7
Figure 7
Schematic diagram of intermediate processing.
See this image and copyright information in PMC

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