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Anomaly Detection in Orthopedic Musculoskeletal Radiographs Using Deep Learning

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Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 693))

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Abstract

In this paper, we investigate anomaly detection in orthopedics musculoskeletal radiographs using deep learning. We examine thirteen models from the most powerful neural network families: generative adversarial networks (GANs), autoencoders (AEs) and convolutional neural network (CNN). The main goal is to detect anomalies in musculoskeletal radiographs using the Stanford Musculoskeletal Radiographs (MURA) dataset. The results of the examined models were compared to several recent studies. Generally, CNN models achieve the best score of 0.822 which is a very promising result, competitive for expert radiologists performance.

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Author information

Authors and Affiliations

  1. IMS Team, ADMIR Laboratory, Rabat IT Center, ENSIAS, Mohammed V University, Rabat, Morocco

    Nabila Ounasser, Maryem Rhanoui & Bouchra El Asri

  2. Meridian Team, LYRICA Laboratory, School of Information Sciences, Rabat, Morocco

    Maryem Rhanoui & Mounia Mikram

Authors
  1. Nabila Ounasser
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  2. Maryem Rhanoui
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  3. Mounia Mikram
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  4. Bouchra El Asri
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Corresponding author

Correspondence to Nabila Ounasser .

Editor information

Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University London, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, University of Reading, England, UK

    R. Simon Sherratt

  3. Department of Computer Science and Engineering, Techno International Newtown, Chakpachuria, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ounasser, N., Rhanoui, M., Mikram, M., El Asri, B. (2023). Anomaly Detection in Orthopedic Musculoskeletal Radiographs Using Deep Learning. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 693. Springer, Singapore. https://doi.org/10.1007/978-981-99-3243-6_8

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  • DOI: https://doi.org/10.1007/978-981-99-3243-6_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3242-9

  • Online ISBN: 978-981-99-3243-6

  • eBook Packages: EngineeringEngineering (R0)

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