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Fine-Grained Wound Tissue Analysis Using Deep Neural Network
Conference proceeding

Fine-Grained Wound Tissue Analysis Using Deep Neural Network

H Nejati, H A Ghazijahani, M Abdollahzadeh, T Malekzadeh, N-M Cheung, K-H Lee and L-L Low
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1010
01/01/2018

Abstract

Acoustics Artificial neural networks Classification Evaluation Feature extraction Image processing Signal processing
Conference Title: ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Conference Start Date: 2018, April 15 Conference End Date: 2018, April 20 Conference Location: Calgary, AB, Canada Tissue assessment for chronic wounds is the basis of wound grading and selection of treatment approaches. While several image processing approaches have been proposed for automatic wound tissue analysis, there has been a shortcoming in these approaches for clinical practices. In particular, seemingly, all previous approaches have assumed only 3 tissue types in the chronic wounds, while these wounds commonly exhibit 7 distinct tissue types that presence of each one changes the treatment procedure. In this paper, for the first time, we investigate the classification of 7 wound tissue types. We work with wound professionals to build a new database of 7 types of wound tissue. We propose to use pre-trained deep neural networks for feature extraction and classification at the patch-level. We perform experiments to demonstrate that our approach outperforms other state-of-the-art. We will make our database publicly available to facilitate research in wound assessment.

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