Wound Tissue Type Classification Using Deep Neural Network

سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 529

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شناسه ملی سند علمی:

WTRMED05_049

تاریخ نمایه سازی: 5 آذر 1397

چکیده مقاله:

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.

نویسندگان

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