A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 6, 2017

Abstract

BACKGROUND AND OBJECTIVES: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning.

Authors

  • Shuchao Pang
    College of Computer Science and Technology, Jilin University, Qianjin Street: 2699, Jilin Province, China; Department of Computing, Macquarie University, Sydney, NSW 2109, Australia. Electronic address: pangshuchao1212@sina.com.
  • Zhezhou Yu
    College of Computer Science and Technology, Jilin University, Qianjin Street: 2699, Jilin Province, China. Electronic address: yuzz@jlu.edu.cn.
  • Mehmet A Orgun
    Department of Computing, Macquarie University, Sydney, NSW 2109, Australia; Faculty of Information Technology, Macau University of Science and Technology, Taipa, Macau. Electronic address: mehmet.orgun@mq.edu.au.

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