Wound Tissue Segmentation and Classification Using U-Net and Random Forest

V. S. Arjun, Leena Chandrasekhar and K. U. Jaseena
Additional contact information
V. S. Arjun: MES College Marampally, (Affiliated to Mahatma Gandhi University, Kottayam), North Vazhakulam, Aluva, Ernakulam 683105, Kerala, India
Leena Chandrasekhar: MES College Marampally, (Affiliated to Mahatma Gandhi University, Kottayam), North Vazhakulam, Aluva, Ernakulam 683105, Kerala, India
K. U. Jaseena: MES College Marampally, (Affiliated to Mahatma Gandhi University, Kottayam), North Vazhakulam, Aluva, Ernakulam 683105, Kerala, India

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 05, 1-16

Abstract: Analysing wound tissue is a crucial research field for assessing the progression of wound healing. Wounds exhibit certain attributes concerning colour and texture, although these features can vary among different wound images. Research in this field serves multiple purposes, including confirming the presence of chronic wounds, identifying infected wounds, determining the origin of the wound and addressing other factors that classify and characterise various types of wounds. Wounds pose a substantial health concern. Currently, clinicians and nurses mainly evaluate the healing status of wounds based on visual examination. This paper presents an outline of digital image processing and traditional machine learning methods for the tissue analysis of chronic wound images. Here, we propose a novel wound tissue analysis system that consists of wound image pre-processing, wound area segmentation and wound analysis by tissue segmentation. The wound area is extracted using a simple U-Net segmentation model. Granulation, slough and necrotic tissues are the three primary forms of wound tissues. The k-means clustering technique is employed to assign labels to tissues. Within the wound boundary, the tissue classification is performed by applying the Random Forest classification algorithm. Both segmentation (U-Net) and classification (Random Forest) models are trained, and the segmentation gives 99% accuracy, and the classification model gives 99.21% accuracy.

Keywords: Wound tissue; segmentation; U-Net; classification; Random Forest; k-means (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S021964922450062X
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:jikmxx:v:23:y:2024:i:05:n:s021964922450062x

Ordering information: This journal article can be ordered from

DOI: 10.1142/S021964922450062X

Access Statistics for this article

Journal of Information & Knowledge Management (JIKM) is currently edited by Professor Suliman Hawamdeh

More articles in Journal of Information & Knowledge Management (JIKM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().