Variable convolutional autoencoder method based on teaching-learning-based optimization for medical image classification

doi:10.11772/j.issn.1001-9081.2021061109

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (2): 592-598.DOI: 10.11772/j.issn.1001-9081.2021061109

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Variable convolutional autoencoder method based on teaching-learning-based optimization for medical image classification

Wei LI¹^,², Yaochi FAN¹(), Qiaoyong JIANG¹, Lei WANG², Qingzheng XU³

^1.School of Computer Science and Engineering，Xi’an University of Technology，Xi’an Shaanxi 710048，China
^2.Shaanxi Key Laboratory for Network Computing and Security Technology （Xi’an University of Technology），Xi’an Shaanxi 710048，China
^3.College of Information and Communication，National University of Defense Technology，Xi’an Shaanxi 710106，China

Received:2021-06-28 Revised:2021-07-14 Accepted:2021-07-15 Online:2022-02-11 Published:2022-02-10
Contact: Yaochi FAN
About author:LI Wei， born in 1973， Ph. D.， associate professor. Her research interests include evolutionary optimization， data mining.
FAN Yaochi， born in 1996， M. S. candidate. His research interests include evolutionary optimization， machine learning.
JIANG Qiaoyong， born in 1987， Ph. D.， lecturer. His research interests include evolutionary optimization.
WANG Lei， born in 1972， Ph. D.， professor. His research interests include evolutionary optimization， big data.
XU Qingzheng， born in 1980， Ph. D.， associate professor. His research interests include evolutionary optimization， optimization theory and application.

基于教与学优化的可变卷积自编码器的医学图像分类方法

李薇¹^,², 樊瑶驰¹(), 江巧永¹, 王磊², 徐庆征³

^1.西安理工大学计算机科学与工程学院, 西安 710048
^2.陕西省网络计算与安全技术重点实验室(西安理工大学), 西安 710048
^3.国防科技大学信息通信学院, 西安 710106

通讯作者: 樊瑶驰
作者简介:李薇（1973—），女，黑龙江哈尔滨人，副教授，博士，CCF会员，主要研究方向：演化优化、数据挖掘；
樊瑶驰（1996—），男，陕西西安人，硕士研究生，CCF学生会员，主要研究方向：演化优化、机器学习；
江巧永（1987—），男，浙江台州人，讲师，博士，CCF会员，主要研究方向：演化优化；
王磊（1972—），男，陕西白水人，教授，博士，CCF高级会员，主要研究方向：演化优化、大数据；
徐庆征（1980—），男，山东临清人，副教授，博士，CCF高级会员，主要研究方向：演化优化、最优化理论与应用。

Abstract

Abstract:

In order to solve the problems such as high time cost， inaccuracy and influence of parameter setting on algorithm performance when optimizing parameters of Convolutional Neural Network （CNN） by traditional manual methods， a variable Convolutional AutoEncoder （CAE） method based on Teaching-Learning-Based Optimization （TLBO） was proposed. In the algorithm， a variable-length individual encoding strategy was designed to quickly construct the CAE structure， and stack CAEs to a CNN. In addition， the excellent individual structure information was fully utilized to guide the algorithm to search the regions with more possibility， thereby improving the algorithm performance. Experimental results show that the classification accuracy of the proposed algorithm achieves 89.84% when solving medical image classification problems， which is higher than those of traditional CNN and similar neural networks. The proposed algorithm solves the medical image classification problems by optimizing the CAE structure and stacking CNN， and effectively improves the classification accuracy of medical image classification.

Key words: Convolutional AutoEncoder (CAE), Convolutional Neural Network (CNN), Teaching-Learning-Based Optimization (TLBO), Evolutionary Algorithm (EA), medical image

摘要：

针对传统手工方法优化卷积神经网络（CNN）参数时存在耗时长、不准确，以及参数设置影响算法性能等问题，提出一种基于教与学优化（TLBO）的可变卷积自编码器（CAE）算法。该算法设计了可变长度的个体编码策略，从而快速构建CAE结构，并堆叠CAE为一个CNN；此外，充分利用优秀个体的结构信息来引导算法朝着更有希望的区域搜索，从而提高算法性能。实验结果表明，所提算法在解决医学图像分类问题时，分类精度达到89.84%，高于传统CNN和同类型神经网络。该算法通过优化CAE结构和堆叠CNN解决医学图像分类问题，有效提高了医学图像分类性能。

关键词: 卷积自编码器, 卷积神经网络, 教与学优化, 演化算法, 医学图像

CLC Number:

TP18

Wei LI, Yaochi FAN, Qiaoyong JIANG, Lei WANG, Qingzheng XU. Variable convolutional autoencoder method based on teaching-learning-based optimization for medical image classification[J]. Journal of Computer Applications, 2022, 42(2): 592-598.

李薇, 樊瑶驰, 江巧永, 王磊, 徐庆征. 基于教与学优化的可变卷积自编码器的医学图像分类方法[J]. 《计算机应用》唯一官方网站, 2022, 42(2): 592-598.

Figures/Tables 11

Fig. 1 Structure of CAE

Fig. 2 Structure of TVCAE

Tab. 1 Encoded information in convolutional layer and pooling layer of TVCAE

类别	编码信息
Convolution layer	the filter size， the number of feature maps， the stride size， the convolutional type， the mean value of filter elements， and the coefficient of L2
Pooling layer	the kernel size， the stride size， the pooling type

Fig. 3 Three individuals with different encoded information from TLBOAO

Fig. 4 Hidden structure coding for TLBOAO

Fig. 5 Average values of three individuals in Figure 3

Fig. 6 Examples from BreastMNIST dataset

Tab. 2 Comparison of classification accuracy of TVCAE， CAE and FCAE for BreastMNIST

算法	分类精度
CAE	0.843 8
FCAE	0.843 8
TVCAE	0.8516

Fig. 7 Classification accuracy trend of one-layer networks

Tab. 3 Comparison of classification accuracy of TVCAE with Alexnet， VGGnet-11， CAE and FCAE for BreastMNIST

算法	分类精度
Alexnet	0.867 2
VGGnet-11	0.890 6
CAE-2	0.867 2
FCAE-2	0.875 0
FCAE-3	0.882 8
TVCAE-2	0.882 8
TVCAE-3	0.8984

Fig. 8 Classification accuracy and accuracy trend of complex networks

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Variable convolutional autoencoder method based on teaching-learning-based optimization for medical image classification

基于教与学优化的可变卷积自编码器的医学图像分类方法

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