• K.Karpaga Priyaa
• J.Shalini
• G.Harini
• R.Preethi

A chronic wound is a wound that fails to respond to treatment after months that increases the risk of complications to health of patient such as diabetes,circulation problem,cancer and infections.The existing system is unable to classify the wounds accurately because it classifies the periwound(area surrounding the wound)as infected area which leads to wrong diagnosis,it includes costly scans and takes long time to classify the wounds.Thus the proposed system reduces the medical fee for diagnosis and it is classifies the wounds without calculating the periwound and has higher accuracy .Support vector machine(svm) and Fuzzy K-Means(FKM) are used for classification of granulation tissue from extracted haemoglobin images.Also the system identifies the depth and height of the wound,stage of the infected wound and also provides solutions to heal the wound. Results obtained indicate an overall accuracy of 96.88% which is better than the existing systems.

Chronic wounds,support vector machine,Fuzzy K-Means

"CHRONIC WOUND DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.550-553, May 2019, Available :http://www.jetir.org/papers/JETIRCU06107.pdf

"CHRONIC WOUND DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp550-553, May 2019, Available at : http://www.jetir.org/papers/JETIRCU06107.pdf