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Classification of Musculoskeletal Abnormalities with Convolutional Neural Networks

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Advances in Bioinformatics and Computational Biology (BSB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12558))

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Abstract

Computer-aided diagnosis has the potential to alleviate the burden on medical doctors and decrease misdiagnosis, but building a successful method for automatic classification is challenging due to insufficient labeled data. In this work, we investigate the usage of convolutional neural networks to diagnose musculoskeletal abnormalities using radiographs (X-rays) of the upper limb and measure the impact of several techniques in our model. We achieved the best results by utilizing an ensemble model that employs a support vector machine to combine different models, resulting in an overall AUC ROC of 0.8791 and Kappa of 0.6724 when evaluated using an independent test set.

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Notes

  1. 1.

    https://stanfordmlgroup.github.io/competitions/mura/.

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Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, Brazil

    Guilherme Tiaki Sassai Sato, Leodécio Braz da Silva Segundo & Zanoni Dias

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  1. Guilherme Tiaki Sassai Sato
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  2. Leodécio Braz da Silva Segundo
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  3. Zanoni Dias
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Correspondence to Guilherme Tiaki Sassai Sato .

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Editors and Affiliations

  1. University of São Paulo, São Paulo, Brazil

    João C. Setubal

  2. Instituto Federal de Goiás, Formosa, Goiás, Brazil

    Waldeyr Mendes Silva

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Sato, G.T.S., da Silva Segundo, L.B., Dias, Z. (2020). Classification of Musculoskeletal Abnormalities with Convolutional Neural Networks. In: Setubal, J.C., Silva, W.M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2020. Lecture Notes in Computer Science(), vol 12558. Springer, Cham. https://doi.org/10.1007/978-3-030-65775-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-65775-8_7

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