Basic Concept of Deep Learning and Its Application Techniques

• SHOUNO Hayaru

  Graduate School of Informatics and Engineering, University of Electro Communications

Bibliographic Information

Other Title

• ディープラーニングの基礎とその関連技術

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.

Journal

• Medical Imaging Technology

  Medical Imaging Technology 35 (4), 180-186, 2017

  The Japanese Society of Medical Imaging Technology

Keywords

• Deep learning
• Deep convolution neural network
• Transfer style learning
• Diffuse lung disease

Details

• CRID: 1390001204653996928
• NII Article ID: 130006108052
• DOI: 10.11409/mit.35.180
• ISSN: 21853193
• ISSN: 0288450X
• Web Site: https://search.jamas.or.jp/link/ui/2018020268
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed