liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0003-2777-9416
Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. (Pattern Recognition)ORCID-id: 0000-0002-4255-5130
2019 (engelsk)Inngår i: Journal of Burn Care & Research, ISSN 1559-047X, E-ISSN 1559-0488, Vol. 40, nr 6, s. 857-863Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Oxford University Press, 2019. Vol. 40, nr 6, s. 857-863
Emneord [en]
Burn depth, time-independent prediction, deep convolutional neural network, artificial intelligence
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-157386DOI: 10.1093/jbcr/irz103ISI: 000495368300020PubMedID: 31187119OAI: oai:DiVA.org:liu-157386DiVA, id: diva2:1322728
Merknad

Funding agencies: Analytic Imaging Diagnostic Arena (AIDA)

Tilgjengelig fra: 2019-06-11 Laget: 2019-06-11 Sist oppdatert: 2019-11-27bibliografisk kontrollert

Open Access i DiVA

Fulltekst tilgjengelig fra 2020-06-11 08:35
Tilgjengelig fra 2020-06-11 08:35

Andre lenker

Forlagets fulltekstPubMed

Personposter BETA

Cirillo, Marco DomenicoMirdell, RobinSjöberg, FolkePham, Tuan

Søk i DiVA

Av forfatter/redaktør
Cirillo, Marco DomenicoMirdell, RobinSjöberg, FolkePham, Tuan
Av organisasjonen
I samme tidsskrift
Journal of Burn Care & Research

Søk utenfor DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric

doi
pubmed
urn-nbn
Totalt: 132 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf