Abstract
Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network
Highlights
Wound management technologies are an essential part of the treatment of chronic wounds, which affect around 6.5 million patients at the cost of $25 billion yearly in the United States. [1]
This study provides a rule of thumb to choose the right number of training images and epochs for generative adversarial network (GAN) algorithms in healthcare applications
This study presents that the conditional GAN (cGAN) algorithm can achieve chronic wound border segmentation and tissue classification efficiently
Summary
Wound management technologies are an essential part of the treatment of chronic wounds, which affect around 6.5 million patients at the cost of $25 billion yearly in the United States. [1]. Wound management technologies are an essential part of the treatment of chronic wounds, which affect around 6.5 million patients at the cost of $25 billion yearly in the United States. Advanced computer vision methods assist the accurate monitoring of wound healing [4]. The computer vision paired with artificial intelligence (AI) would provide caregivers with continuous and accurate wound healing monitoring at a lower cost. Familiarity with wound tissue types and their sizes play an important role in determining the right chronic wound treatment plan. One of the goals of this study is to contribute to the development of such a system for wound border segmentation and tissue classification utilising the conditional generative adversarial network (GAN) algorithm in a hybrid way
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