Paper Info
Title
Chronic Wound Characterization using Bayesian Classifier under Telemedicine Framework
Abstract
AbstractChronic wound CW treatment by large is a burden for the government and society due to its high cost and time consuming treatment. It becomes more serious for the old age patient with the lack of moving flexibility. Proper wound recovery management is needed to resolve this problem. Careful and accurate documentation is required for identifying the patient's improvement and or deterioration timely for early diagnostic purposes. This paper discusses the comprehensive wound diagnostic method using three important modules, viz. Wounds Data Acquisition WDA module, Tele-Wound Technology Network TWTN module and Wound Screening and Diagnostic WSD module. Here the wound image characterization and diagnosis tool has been proposed under telemedicine to classify the percentage wise wound tissue based on the color variation over regular intervals for providing a prognostic treatment with better degree of accuracy. The Bayesian classifier based wound characterization BWC technique is proposed that able to identify wounded tissue and correctly predict the wound status with a good degree of accuracy. Results show that BWC technique provides very good accuracy, i.e. 87.40%, whereas the individual tissue wise accuracy for granulation tissue is 89.44%, slough tissue is 81.87% and for necrotic tissue is 90.91%.
Year
DOI
Venue
2016
10.4018/IJEHMC.2016010105
Periodicals
Keywords
Field
DocType
Chronic Wound Assessment, Fuzzy C-means, K-means, Naive Bayesian Classifier, Telemedicine
Telemedicine,Chronic wound,Naive Bayes classifier,Computer security,Computer science,Artificial intelligence,Surgery,Granulation tissue,Wound status,Machine learning,Naive bayesian classifier
Journal
Volume
Issue
ISSN
7
1
1947-315X
Citations 
PageRank 
References 
1
0.35
10
Authors
3
Name
Order
Citations
PageRank
Chinmay Chakraborty1132.78
Gupta, B.2224.18
Soumya Kanti Ghosh334539.91