软件导刊2023，Vol.22Issue(12)：84-91,8.DOI:10.11907/rjdk.231123
基于人工智能算法STN-GResnet的肝硬化识别
Liver Cirrhosis Recognition Based on Artificial Intelligence Algorithm STN-GResnet
摘要
Abstract
Aiming at the problems of weak generalization,low recognition rate,large amount of parameters and low quality of feature extrac-tion of traditional neural network,a new model structure named STN-GResnet is proposed in this paper.Using part of the Resnet18 network structure and adding the Ghostmodule to enhance convolution features.Combined with the spatial transformation network,the extracted fea-tures are space invariant.Meanwhile,the additional angle margin loss function(ArcFace)is added to this network model to train and optimize loss.The difference of texture and granularity of ultrasonic liver CT image is enhanced by optimizing the characteristics of categories.The pre training parameters of transfer learning are used,and then the sample set in ultrasonic liver CT images is improved through data enhancement to avoid the phenomenon of model over fitting caused by insufficient sample size.Finally,the objective recognition rate of the model is 95.7%.What's more,the model is small and the operation efficiency is high.关键词
深度学习/医学图像识别/迁移学习/数据增强Key words
deep learning/medical image recognition/transfer learning/data enhancement分类
信息技术与安全科学引用本文复制引用
鞠维欣,赵希梅..基于人工智能算法STN-GResnet的肝硬化识别[J].软件导刊,2023,22(12):84-91,8.基金项目
国家自然科学基金项目(61303079) （61303079）
