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A Survey of Deep Learning Based Methods in Medical Image Processing

Author(s):

Yinglei Song*, Mohammad N.A. Rana, Junfeng Qu and Chunmei Liu   Pages 1 - 14 ( 14 )

Abstract:


Background: Recently, deep learning based methods have become an important approach to the accurate analysis of medical images.

Methods: This paper provides a comprehensive survey of the most important deep learning based methods that have been developed for medical image processing. A number of important contributions made in last five years are summarized and surveyed.

Results: Specifically, deep learning based algorithms developed for image segmentation, image classification, registration, object detection and other important problems are reviewed. In addition, an overview of challenges that currently exist in the field and potential directions for future research is provided in the end of the survey.

Keywords:

Medical Image Processing, Deep Learning Models, Review, Convolutional Neural Networks, Recurrent Neural Networks

Affiliation:

School of Electronics and Information Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, School of Electronics and Information Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, Department of Computer Science and Information Technology, Clayton State University, Morrow, GA 30260, Department of Electrical Engineering and Computer Science, Howard University, Washington DC, 20059



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