计算机工程与应用2019，Vol.55Issue(5)：112-117,6.DOI:10.3778/j.issn.1002-8331.1804-0300
深度学习模型GoolgeNet-PNN对肝硬化的识别
Cirrhosis Recognition by Deep Learning Model GoolgeNet-PNN
摘要
Abstract
Traditional machine learning is difficult to extract high quality features and it consumes much time and energy, Therefore, based on the deep learning method and combined with convolution neural network and probabilistic neural network, a new model called GoolgeNet-PNN is first put forward and applied. Firstly, it automatically learns features and avoids the complexity of manually extracting features. Secondly, it combines the advantages of PNN, such as easy training and fast convergence speed. It has achieved good results in the experiment of liver disease classification. What’s more, combined with the migrating learning, the method firstly pre-trains in the natural image set and then is applied to the medical image, which avoids the overfitting problem caused by the shortage of samples. Finally, experimental results show recognition accuracy is better than other methods and it has reached 98% objectively.关键词
深度学习/医学图像/卷积神经网络/概率神经网络/迁移学习Key words
deep learning/ medical image/ convolution neural network/ probabilistic neural network/ transfer learning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,魏宾,王国栋..深度学习模型GoolgeNet-PNN对肝硬化的识别[J].计算机工程与应用,2019,55(5):112-117,6.基金项目
湖南省教育厅科研项目(No.16C0040). （No.16C0040）
