Abstract
In this paper, we explain about a basic architecture and learning style of deep convolution neural network (DCNN), which is known as a kind of deep learning (DL) system, and also show an application of medical image classification. The DCNN is a combination of neural network architecture called “Neocognitron” and learning method called error back propagation (BP). One of the important factor for the performance of DCNN is a balance between the number of the free parameters in the network and the scale of the training dataset. In several field such like medical imaging, it is hard to acquire labeled data. The small dataset sometimes occur the overtraining. In order to prevent the overtraining, we introduce a transfer style learning method into the DCNN, which improves the classification performance.
