A comparative study of the classification of skin burn depth in human - Kuan, Pei Nei; Li, Stephanie Chua Hui; Ehfa, binti Bujang Safawi; Wang, Hui Hui; Tiong, Wei King (Universiti Teknikal Malaysia Melaka, 2017)
A correct first evaluation of skin burn injury is essential as it is an important step in providing the first treatment to the patient by determining the burn depths. The objective of this paper is to conduct a comparative study of different types of classification algorithms on the classification of different burn depths by using an image mining approach. 20 classification algorithms were compared on a skin burn dataset comprising skin burn images categorized into three classes by medical experts. The dataset was evaluated using both a supplied test set and 10-fold cross validation methods. Empirical results showed that the best classification algorithms that were able to classify most of the burn depths using a supplied test set were Logistic, Simple Logistic, MultiClassClassifier, OneR, and LMT, with an average accuracy of 68.9% whereas for 10-fold cross validation evaluation, the best result was obtained through the Simple Logistic algorithm with an average accuracy of 73.2%. It can be concluded that Simple Logistic has the potential to provide the best classification for the degree of skin burn depth.