基于SAE框架的皮肤病变图像生成与分类  

Skin Lesion Image Generation and Classification Based on SAE Framework

作　　者：赵宇航 闫天星 伊力哈木·亚尔买买提[1] ZHAO Yu-hang;YAN Tian-xing;YILIHAMU YAErmaimaiti(School of Electrical Engineering,Xinjiang University,Urumqi Xinjiang 830047,China)

机构地区：[1]新疆大学电气工程学院,新疆乌鲁木齐830047

出　　处：《计算机仿真》2024年第11期278-285,431,共9页Computer Simulation

基　　金：国家自然科学基金(61866037,61462082)。

摘　　要：针对现有算法对于皮肤病变数据集形态复杂、各类样本不平衡导致分类精度低、特征提取能力不强等问题,提出了一种基于皮肤病变图像的风格对抗生成网络与设计的ECA-ConvNext分类网络结合的皮肤病变图像生成分类方法(SL-style-GAN2 and ECA-ConvNeXt Frame,SAE)。首先,对风格对抗生成网络中对生成器重新设计,并且对判别器部分进行重构,使判别器可以同时为生成器提供局部和全局信息,从而生成更好的样本图片以供后续分类模型得到更好的效果。之后选用ConvNeXt-T为分类基础网络,设计了深层特征提取模块(Deep information extraction module,DIEM)使通道和权值之间直接联系,提高网络特征提取能力,从而提高模型精度。最后,在ISIC 2018数据集上进行实验,实验结果表明,分类准确率达到94.0%,比原始ConvNeXt提高了4.5%。A skin lesion image generation and classification method(SL-styleGAN2 and ECA ConvNeXt Frame,SAE)was proposed,which combines a style adversarial generation network based on skin lesion images with a de-signed ECA ConvNext classification network,to address the problems of low classification accuracy and weak feature extraction ability caused by the complex morphology and imbalanced samples of existing algorithms on skin lesion datasets.Firstly,the generator was redesigned in the style confrontation generation network,and the discriminator part was reconstructed,so that the discriminator could provide both local and global information to the generator,so as to generate better sample pictures for the subsequent classification model to get better results.Then,the ConvNeXt-T is selected as the classification basis network,and the Deep information extraction module is designed to make the chan-nel and weight directly related to improve the feature extraction ability of the network,so as to improve the accuracy of the model.Finally,the experimental results on ISIC 2018 dataset show that the classification accuracy reaches 94.0%,which is 4.5%higher than the original ConvNeXt.

关 键 词：图像处理 皮肤病变分类 生成对抗网络 高效通道注意力 计算机辅助识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]