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Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-2777-9416
Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. (Pattern Recognition)ORCID iD: 0000-0002-4255-5130
2019 (English)In: Journal of Burn Care & Research, ISSN 1559-047X, E-ISSN 1559-0488, Vol. 40, no 6, p. 857-863Article in journal (Refereed) Published
Abstract [en]

We present in this paper the application of deep convolutional neural networks, which are a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Colour images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pre-trained deep convolutional neural networks: VGG-16, GoogleNet, ResNet-50, and ResNet-101. In the end, the best 10-fold cross-validation results obtained from ResNet- 101 with an average, minimum, and maximum accuracy are 81.66%, 72.06% and 88.06%, respectively; and the average accuracy, sensitivity and specificity for the four different types of burn depth are 90.54%, 74.35% and 94.25%, respectively. The accuracy was compared to the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and therefore can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.

Place, publisher, year, edition, pages
Oxford University Press, 2019. Vol. 40, no 6, p. 857-863
Keywords [en]
Burn depth, time-independent prediction, deep convolutional neural network, artificial intelligence
National Category
Surgery Medical Image Processing Other Clinical Medicine
Identifiers
URN: urn:nbn:se:liu:diva-157386DOI: 10.1093/jbcr/irz103ISI: 000495368300020PubMedID: 31187119OAI: oai:DiVA.org:liu-157386DiVA, id: diva2:1322728
Note

Funding agencies: Analytic Imaging Diagnostic Arena (AIDA)

Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-11-27Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2020-06-11 08:35
Available from 2020-06-11 08:35

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Authority records BETA

Cirillo, Marco DomenicoMirdell, RobinSjöberg, FolkePham, Tuan

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Cirillo, Marco DomenicoMirdell, RobinSjöberg, FolkePham, Tuan
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Division of Biomedical EngineeringFaculty of Science & EngineeringDivision of Surgery, Orthopedics and OncologyFaculty of Medicine and Health SciencesDepartment of Hand and Plastic Surgery
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Journal of Burn Care & Research
SurgeryMedical Image ProcessingOther Clinical Medicine

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