Wound Tissue Type Classification Using Deep Neural Network
Publish place: Third International Congress and Fifth National Congress on Wound and Tissue Restoration
Publish Year: 1397
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
WTRMED05_049
تاریخ نمایه سازی: 5 آذر 1397
Abstract:
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 issue 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.
Keywords:
Authors
Hamed Alizadeh Ghazijahani
PHD, Student of Image Processing; Artificial Intelligence, Singapore University of Technology and Design (SUTD), Singapore, Singapore
Hossein Nejati
PHD, Student of Image Processing; Artificial Intelligence, Singapore University of Technology and Design (SUTD), Singapore, Singapore
Milad Abdollahzadeh
PHD, Student of Image Processing; Artificial Intelligence, Singapore University of Technology and Design (SUTD), Singapore, Singapore
Touba Malekzadeh
PHD, Student of Image Processing; Artificial Intelligence, Singapore University of Technology and Design (SUTD), Singapore, Singapore