Time-Independent Prediction of Burn Depth Using Deep Convolutional Neural Networks

Author:

Cirillo Marco Domenico12ORCID,Mirdell Robin345,Sjöberg Folke345,Pham Tuan D12

Affiliation:

1. Department of Biomedical Engineering, Linköping University, Sweden

2. Center for Medical Image Science and Visualization, Linköping University, Sweden

3. The Burn Centre, Linköping University Hospital, Sweden

4. Department of Plastic Surgery, Hand Surgery, and Burns, Linköping University, Sweden

5. Department of Clinical and Experimental Medicine, Linköping University, Sweden

Abstract

Abstract We present in this paper the application of deep convolutional neural networks (CNNs), which is a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Color 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 pretrained deep CNNs: 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 with 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.

Publisher

Oxford University Press (OUP)

Subject

Rehabilitation,Emergency Medicine,Surgery

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