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    Burns Depth Assessment Using Deep Learning Features

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    Publication date
    2020-12
    Author
    Abubakar, Aliyu
    Ugail, Hassan
    Smith, K.M.
    Bukar, Ali M.
    Elmahmudi, Ali
    Keyword
    Skin burns
    Burn depths
    Deep learning
    Features
    SVM
    Classification
    Rights
    (c) 2020 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY licence (http://creativecommons.org/licenses/by/4.0/)
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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    Abstract
    Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method: In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results: The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion: The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.
    URI
    http://hdl.handle.net/10454/18941
    Version
    Published version
    Citation
    Abubakar A, Ugail H, Smith KM et al (2020) Burns Depth Assessment Using Deep Learning Features. Journal of Medical and Biological Engineering. 40(6): 923-933.
    Link to publisher’s version
    https://doi.org/10.1007/s40846-020-00574-z
    Type
    Article
    Collections
    Engineering and Informatics Publications

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