Encoder–decoder semantic segmentation models for pressure wound images

ABSTRACT

Segmentation of wound images is important for efficient wound treatment so that appropriate treatment methods can be recommended quickly. Wound measurement, is subjective for an overall assessment. The establishment of a high-performance automatic segmentation system is of great importance for wound care. The use of machine learning methods will make performing wound segmentation with high performance possible. Great success can be achieved with deep learning, which is a sub-branch of machine learning and has been used in the analysis of images recently (classification, segmentation, etc.). In this study, pressure wound segmentation was discussed with different encoder-decoder based segmentation models. All methods are implemented on the Medetec pressure wound image dataset. In the experiments, FCN, PSP, UNet, SegNet and DeepLabV3 segmentation architectures were used on a five-fold cross-validation. Performances of the models were measured in the experiments and it was demonstrated that the most successful architecture was MobileNet-UNet with 99.67% accuracy.

KEYWORDS:

• Pressure wounds segmentation
• encoder–decoder segmentation models
• deep learning
• transfer learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Hüseyin Eldem

Hüseyin Eldem, was born in Konya, TURKEY in 1981. In 2005, he received his B.Sc. degree in Computer Engineering from Selçuk University. He obtained his M.Sc. degree in Computer Engineering from Selçuk University on the optimization problems.

Erkan Ülker

Prof. Dr. Erkan Ülker, was born in Isparta, TURKEY in 1977. He received his B.Sc. degree in Computer Engineering from Selçuk University. He obtained his M.Sc. degree in Computer Engineering from Selçuk University on the surface of model. He obtained his Ph.D degree in Computer Engineering from Selçuk University on the Artificial Intelligence.

Osman Yaşar Işıklı

Dr. Osman Yaşar Işıklı, was born in Konya in 1971. In 1998, He received his M.D.Ph.D degree from Erciyes University, Faculty of Medicine. In 2012, He completed his expertise at Pamukkale University Faculty of Medicine with the thesis on the Boyd annuloplasty in patients with functional tricuspid regurgitation in th mid-term results.