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

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。

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

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.