research-article

Long bone fracture type classification for limited number of CT data with deep learning

Authors:

Keon Myung Lee,

Seung Myung ChoiAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 1090 - 1095

https://doi.org/10.1145/3341105.3373900

Published: 30 March 2020 Publication History

Abstract

High-energy collisions and sports injuries may result in long bone fractures. To treat and manage fracture patients, it is necessary for trauma surgeons and physicians to first classify their fracture type because their treatment is different according to their type. Facture classification demands high level of expertise and careful examination. To assist the fracture treatment in medical practice, this paper proposes a convolutional neural network-based classification method to classify lower long bone fractures in which output class label can be multiple, data size is small compared to the number of class labels, and classes in the data set are imbalanced. It presents how to organize the deep network model, how to prepare the data, how to augment data, how to determine the classes from the network output, and how to evaluate the performance of model. It also explains the characteristics of the computed tomography (CT) image data. In the experiments, the proposed method showed 80.6% precision and 92.0% recall for a data set of CT fracture images.

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Cited By

Olczak JPrijs JIJpma FWallin FAkbarian EDoornberg JGordon M(2024)External validation of an artificial intelligence multi-label deep learning model capable of ankle fracture classificationBMC Musculoskeletal Disorders10.1186/s12891-024-07884-225:1Online publication date: 4-Oct-2024
https://doi.org/10.1186/s12891-024-07884-2
Shandilya GAnand VGupta S(2024)Unified CNN Approach for Fracture Detection Across All Anatomical Regions in Radiographic Images2024 Global Conference on Communications and Information Technologies (GCCIT)10.1109/GCCIT63234.2024.10862367(1-6)Online publication date: 25-Oct-2024
https://doi.org/10.1109/GCCIT63234.2024.10862367
Sani ZPrasad RHashim E(2023)Breast Cancer Classification Using Equivariance Transition in Group Convolutional Neural NetworksIEEE Access10.1109/ACCESS.2023.325364011(28454-28465)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3253640
Show More Cited By

Index Terms

Long bone fracture type classification for limited number of CT data with deep learning
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 March 2020

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National Research Foundation of Korea, Republic of Korea

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SAC '20

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SIGAPP

SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Olczak JPrijs JIJpma FWallin FAkbarian EDoornberg JGordon M(2024)External validation of an artificial intelligence multi-label deep learning model capable of ankle fracture classificationBMC Musculoskeletal Disorders10.1186/s12891-024-07884-225:1Online publication date: 4-Oct-2024
https://doi.org/10.1186/s12891-024-07884-2
Shandilya GAnand VGupta S(2024)Unified CNN Approach for Fracture Detection Across All Anatomical Regions in Radiographic Images2024 Global Conference on Communications and Information Technologies (GCCIT)10.1109/GCCIT63234.2024.10862367(1-6)Online publication date: 25-Oct-2024
https://doi.org/10.1109/GCCIT63234.2024.10862367
Sani ZPrasad RHashim E(2023)Breast Cancer Classification Using Equivariance Transition in Group Convolutional Neural NetworksIEEE Access10.1109/ACCESS.2023.325364011(28454-28465)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3253640
Yadav D(2022)Human Bone Assessment: A Deep Convolutional Neural Network ApproachInternational Conference on Artificial Intelligence and Sustainable Engineering10.1007/978-981-16-8542-2_18(223-233)Online publication date: 30-Apr-2022
https://doi.org/10.1007/978-981-16-8542-2_18
Chen JYu HFeng RChen Dgg J(2020)Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM49941.2020.9313408(534-541)Online publication date: 16-Dec-2020
https://doi.org/10.1109/BIBM49941.2020.9313408

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