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

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 GoogleNet-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.

Key words: deep learning, medical image, convolution neural network, probabilistic neural network, transfer learning