HipXNet: Deep Learning Approaches to Detect Aseptic Loos-Ening of Hip Implants Using X-Ray Images-Reference-Cited by-同舟云学术

HipXNet: Deep Learning Approaches to Detect Aseptic Loos-Ening of Hip Implants Using X-Ray Images

Published:2022 Issue: Volume:10 Page:53359-53373
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Rahman Tawsifur¹^ORCID,Khandakar Amith¹^ORCID,Islam Khandaker Reajul²^ORCID,Soliman Md Mohiuddin³^ORCID,Islam Mohammad Tariqul³^ORCID,Elsayed Ahmed⁴,Qiblawey Yazan¹^ORCID,Mahmud Sakib¹^ORCID,Rahman Ashiqur⁵,Musharavati Farayi⁶,Zalnezhad Erfan⁷,Chowdhury Muhammad E. H.¹^ORCID

Affiliation:

1. Department of Electrical Engineering, Qatar University, Doha, Qatar

2. Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh

3. Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

4. Orthopaedic Department, Hamad Medical Corporation (HMC), Doha, Qatar

5. Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Sendai, Japan

6. Department of Mechanical and Industrial Engineering, Qatar University, Doha, Qatar

7. Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA

Funder

Qatar National Research Fund

Qatar National Library

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09770775.pdf?arnumber=9770775

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