Chronic Wound Characterization Using Bayesian Classifier under Telemedicine Framework

Abstract

Chronic 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%.