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Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Finding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morpholo...

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Detalles Bibliográficos
Autores principales: Hu, Xiaobin, Song, Jian, Liao, Zhenhua, Liu, Yuhong, Gao, Jian, Menze, Bjoern, Liu, Weiqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Tsinghua University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372229/
http://dx.doi.org/10.1007/s40544-021-0516-2
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author Hu, Xiaobin
Song, Jian
Liao, Zhenhua
Liu, Yuhong
Gao, Jian
Menze, Bjoern
Liu, Weiqiang
author_facet Hu, Xiaobin
Song, Jian
Liao, Zhenhua
Liu, Yuhong
Gao, Jian
Menze, Bjoern
Liu, Weiqiang
author_sort Hu, Xiaobin
collection PubMed
description Finding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary Material is available in the online version of this article at 10.1007/s40544-021-0516-2.
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spelling pubmed-83722292021-08-19 Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints Hu, Xiaobin Song, Jian Liao, Zhenhua Liu, Yuhong Gao, Jian Menze, Bjoern Liu, Weiqiang Friction Research Article Finding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary Material is available in the online version of this article at 10.1007/s40544-021-0516-2. Tsinghua University Press 2021-08-18 2022 /pmc/articles/PMC8372229/ http://dx.doi.org/10.1007/s40544-021-0516-2 Text en © The author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Hu, Xiaobin
Song, Jian
Liao, Zhenhua
Liu, Yuhong
Gao, Jian
Menze, Bjoern
Liu, Weiqiang
Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
title Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
title_full Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
title_fullStr Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
title_full_unstemmed Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
title_short Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints
title_sort morphological residual convolutional neural network (m-rcnn) for intelligent recognition of wear particles from artificial joints
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8372229/
http://dx.doi.org/10.1007/s40544-021-0516-2
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