基于多尺度特征与通道特征融合的脑肿瘤良恶性分类模型 -

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基于多尺度特征与通道特征融合的脑肿瘤良恶性分类模型

doi:10.37188/CO.2022-0067

长春理工大学生命科学技术学院, 吉林长春 130022

基金项目:吉林省科技发展计划项目（No. 20200404219YY）

详细信息

作者简介:
姜林奇（1999—），女，内蒙古呼伦贝尔人，硕士研究生，2019年于南华大学获得学士学位，主要从事深度学习、图像分类等方面的研究。E-mail：631540532@qq.com
宁春玉（1976—），女，吉林农安人，硕士，副教授，硕士生导师，2005年于吉林大学获得计算机应用技术硕士学位，主要从事图像处理、模式识别的研究。E-mail：yeningcy@163.com

中图分类号:TP391.4
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- 被引次数:0
出版历程
- 收稿日期:2022-04-12
- 录用日期:2022-08-24
- 修回日期:2022-05-03
- 网络出版日期:2022-08-24

Classification model based on fusion of multi-scale feature and channel feature for benign and malignant brain tumors

School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, China

Funds:Supported by the Science and Technology Development Project of Jilin Province (No. 20200404219YY)

More Information

Corresponding author:yeningcy@163.com

摘要

摘要:
针对脑肿瘤良恶性分类过程复杂、分类准确率不高等问题，提出了一种基于多尺度特征与通道特征融合的分类模型。该模型以ResNeXt网络为主干网络，首先，将基于空洞卷积的多尺度特征提取模块代替第一层卷积层，利用膨胀率获取不同感受野的图像信息，将全局特征与局部显著特征相结合；其次，添加通道注意力机制模块，融合特征通道信息，提高对肿瘤区域的关注度，降低对冗余信息的关注度；最后，采用学习率的线性衰减策略、图像的标签平滑策略以及基于医学图像的迁移学习策略的组合优化提高模型的学习能力和泛化能力。在BraTS2017和BraTS2019数据集中进行实验，准确率分别达到98.11%和98.72%。与经典模型和其他先进方法相比，该分类模型能够有效地减少分类过程的复杂度，提高脑肿瘤良恶性分类的准确率。
- 脑肿瘤/
- 多尺度特征/
- 通道注意力机制/
- 深度学习
Abstract:
Aiming at the problems of complex and inaccurate classification of benign and malignant brain tumors, a classification model was proposed based on the fusion of multi-scale and channel features. The model used ResNeXt as the backbone network. First, the multi-scale feature extraction module based on dilated convolution was used to replace the first convolution layer, which can make full use of dilation rates to obtain the image information from different receptive fields, and combine the global features with significant subtle ones. Second, the channel attention mechanism module was added in the network to fuse the feature channel information in order to increase the attention to the tumor, and reduce the attention to redundant information. Finally, the combination optimization strategy, the MultiStepLR strategy of the learning rate, the label smoothing strategy and the transfer learning strategy on medical images were adopted to improve the learning and generalization abilities of the model. The experiments were carried out on BraTS2017 Dataset and BraTS2019 Dataset, and the classification accuracy were 98.11% and 98.72%, respectively. Compared with other advanced methods and classical models, the proposed classification model can effectively reduce the complexity of the classification process and improve the detection accuracy of benign and malignant brain tumors.
- brain tumor/
- multi-scale feature/
- channel attention mechanism/
- deep learning

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图 1MDCA-ResNeXt网络结构

Figure 1.MDCA-ResNeXt network structure

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图 2 $ C = 32 $ 的ResNeXt结构^[20]

Figure 2.ResNeXt structure with $ C = 32 $ ^[20]

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图 3不同膨胀率的空洞卷积

Figure 3.Dilated convolution results with different dilation rates

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图 4MD模块

Figure 4.MD module

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图 5CA模块

Figure 5.CA module

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图 64种模态下的HGG图像

Figure 6.HGG images in four modalities

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图 74种模态下的LGG图像

Figure 7.LGG images in four modalities

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图 8去除噪声前后对比图

Figure 8.Comparison before and after preprocessing

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图 93种网络的分类结果评价图

Figure 9.Evaluation diagram of classification results for three kinds of Nets

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图 10HGG的原始图像和特征可视化图

Figure 10.Original image and feature visualizations of HGG

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图 11LGG的原始图像和特征可视化图

Figure 11.Original image and feature visualization of LGG

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表 1实验数据集分布

Table 1.Distribution of experimental datasets

数据集	肿瘤类别	数据分布		总数
数据集	肿瘤类别	训练集	测试集	总数
BraTS2017 数据集	HGG	840	210	1050
BraTS2017 数据集	LGG	900	225	1125
BraTS2019 数据集	HGG	1035	260	1295
BraTS2019 数据集	LGG	915	225	1140

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表 2优化前BraTS2017数据集的分类结果评价表

Table 2.Evaluation of classification results on BraTS2017 before optimization

网络	ACC（%）	SEN（%）	SPE（%）	PPV（%）	NPV（%）
ResNet	89.15±1.83	88.76±3.38	89.51±3.38	88.92±3.67	89.60±2.58
SENet	90.44±3.25	93.05±2.09	89.25±7.41	88.21±5.69	93.19±1.77
ResNeXt	90.34±1.14	89.23±3.36	92.53±2.18	91.85±1.96	90.22±2.57
MDCA-ResNeXt	93.19±0.35	93.05±1.67	93.33±1.69	92.91±1.54	93.54±1.36

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表 3优化前BraTS2019数据集的分类结果评价表

Table 3.Evaluation of classification results on BraTS2019 before optimization

网络	ACC（%）	SEN（%）	SPE（%）	PPV（%）	NPV（%）
ResNet	91.83±2.73	93.31±2.12	90.13±4.59	91.71±3.65	92.10±2.51
SENet	91.91±2.42	90.54±5.63	91.74±4.83	93.00±3.76	93.25±2.54
ResNeXt	93.57±1.50	94.23±2.02	92.80±2.99	93.85±2.31	93.35±2.15
MDCA-ResNeXt	94.10±1.40	94.38±1.67	93.78±2.33	94.76±2.12	93.60±1.59

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表 4优化后BraTS2017数据集的分类结果评价表

Table 4.Evaluation of classification results on BraTS2017 after optimization

网络	ACC（%）	SEN（%）	SPE（%）	PPV（%）	NPV（%）
Improved ResNet	96.87±1.49	96.76±0.71	96.98±2.90	96.84±2.97	96.98±0.64
Improved SENet	97.56±1.04	96.67±0.89	98.40±1.43	98.27±1.52	96.94±0.83
Improved ResNeXt	97.98±1.33	97.43±2.06	98.49±1.28	98.38±1.39	97.63±1.88
Improved MDCA-ResNeXt	98.11±0.41	97.43±0.26	98.76±0.91	98.66±0.97	97.63±0.22

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表 5优化后BraTS2019数据集的分类结果评价表

Table 5.Evaluation of classification results on BraTS2019 after optimization

网络	ACC（%）	SEN（%）	SPE（%）	PPV（%）	NPV（%）
Improved ResNet	97.03±1.95	97.31±2.02	96.62±3.04	97.20±2.55	96.91±2.23
Improved SENet	96.69±0.88	94.38±1.89	98.74±0.93	98.62±1.01	94.99±1.57
Improved ResNeXt	97.98±0.57	97.69±0.47	98.31±0.73	98.53±0.64	97.36±0.54
Improved MDCA-ResNeXt	98.72±0.31	98.62±0.64	98.85±0.51	99.00±0.44	98.41±0.73

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表 6先进方法分类结果对比表

Table 6.Comparison of classification results of advanced methods

文献	方法	肿瘤分割	数据集	准确率(%)
文献[7]	HCS+ Multi-SVNN	是	BraTs2014	93.00
文献[15]	Inception V3+POS	是	BraTs2017	96.90
文献[16]	VGG16+ELM	否	BraTs2017	96.90
文献[17]	3D CNN+VGG19+FNN	是	BraTs2017	96.97
文献[8]	FBSO	是	BraTs2018	93.85
文献[19]	3D U-Net	否	BraTs2018	91.67
本文方法	Improved MDCA-ResNeXt	否	BraTs2017	98.11
本文方法	Improved MDCA-ResNeXt	否	BraTs2019	98.72

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