Investigation and Classification of Chronic Wound Tissue images Using Random Forest Algorithm (RF)

Document Type : Research Paper


1 Department of ECE, Christian College of Engineering \& Technology, Oddanchatram near, Pin code - 624619, Tamilnadu.

2 Department of CSE, Christian College of Engineering & Technology, Oddanchatram near, Pin code - 624619, Tamilnadu.

3 Department of Electronics and Communication Engineering, Siddhartha Institute of Technology and Sciences, Narapally, Pin Code - Hyderabad-500088.


The broad increase make use of digital cameras, by hand wound imaging has turn out to be common practice in experimental place. There is in malice of still a condition for a reasonable device for accurate wound curing consideration between dimensional facility and tissue categorization in a exacting simple to exploit technique We achieved the major unit of this plan by computing a 3-D model for wound dimensions using un calibrated revelation techniques. We highlight at this point on tissue classification from color and eminence region descriptors computed after unverified segmentation. As a result of perception distortions, unconstrained lighting provisions and viewpoints, wound assessments modify commonly in the middle of patient review. The majority significant separation of this article is to overcome this trouble by means of a multi inspection approach for tissue classification, relying on a 3-D model onto which tissue labels are mapped and categorization result merged. The investigational categorization tests communicate that improved repeatability and robustness are obtained and that metric assessment is attain through appropriate region and degree dimensions and wound chart origin.
In this manuscript we proposed wound image segmentation, tissue classification in grouping with the Random Forest (RF). These methodology are helpful for classifying the rate of injured tissue in a segmented element and improved accuracy.


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Volume 13, Issue 1
March 2022
Pages 643-651
  • Receive Date: 03 April 2021
  • Revise Date: 03 September 2021
  • Accept Date: 23 September 2021