Literature DB >> 29441773

[Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

T H Hu1,2, L Wan2, T A Liu3, M W Wang2, T Chen1, Y H Wang2.   

Abstract

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Keywords:  age determination by skeleton; deep learning; forensic anthropology; image processing, computer-assisted; image recognition; neural networks (computer); review

Mesh:

Year:  2017        PMID: 29441773     DOI: 10.3969/j.issn.1004-5619.2017.06.013

Source DB:  PubMed          Journal:  Fa Yi Xue Za Zhi        ISSN: 1004-5619


  2 in total

1.  Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.

Authors:  Fengdan Wang; Xiao Gu; Shi Chen; Yongliang Liu; Qing Shen; Hui Pan; Lei Shi; Zhengyu Jin
Journal:  PeerJ       Date:  2020-04-01       Impact factor: 2.984

2.  How do people think about the implementation of speech and video recognition technology in emergency medical practice?

Authors:  Ki Hong Kim; Ki Jeong Hong; Sang Do Shin; Young Sun Ro; Kyoung Jun Song; Tae Han Kim; Jeong Ho Park; Joo Jeong
Journal:  PLoS One       Date:  2022-09-23       Impact factor: 3.752

  2 in total

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