Sofia Zahia1, Daniel Sierra-Sosa2, Begonya Garcia-Zapirain3, Adel Elmaghraby4. 1. Department of Computer Engineering and Computer Science, Duthie Center for Engineering, University of Louisville, Louisville, KY 40292, United States; eVida research laboratory, University of Deusto, Bilbao 48007, Spain. 2. Department of Computer Engineering and Computer Science, Duthie Center for Engineering, University of Louisville, Louisville, KY 40292, United States. Electronic address: d.sierrasosa@louisville.edu. 3. eVida research laboratory, University of Deusto, Bilbao 48007, Spain. 4. Department of Computer Engineering and Computer Science, Duthie Center for Engineering, University of Louisville, Louisville, KY 40292, United States.
Abstract
BACKGROUND AND OBJECTIVES: This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. METHODS: Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. RESULTS: The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. CONCLUSIONS: Our system has been proven to make recognition of complicated structures in biomedical images feasible.
BACKGROUND AND OBJECTIVES: This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. METHODS: Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. RESULTS: The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. CONCLUSIONS: Our system has been proven to make recognition of complicated structures in biomedical images feasible.
Authors: Che Wei Chang; Mesakh Christian; Dun Hao Chang; Feipei Lai; Tom J Liu; Yo Shen Chen; Wei Jen Chen Journal: PLoS One Date: 2022-02-17 Impact factor: 3.240