Abstract
Burn is a devastating injury affecting over eleven million people worldwide and more than 265,000 affected individuals lost their lives every year. Low- and middle-income countries (LMICs) have surging cases of more than 90% of the total global incidences due to poor socioeconomic conditions, lack of preventive measures, reliance on subjective and inaccurate assessment techniques and lack of access to nearby hospitals. These factors necessitate the need for a better objective and cost-effective assessment technique that can be easily deployed in remote areas and hospitals where expertise and reliable burn evaluation is lacking. Therefore, this study proposes the use of Convolutional Neural Network (CNN) features along with different classification algorithms to discriminate between burnt and healthy skin using dataset from Black-African patients. A pretrained CNN model (VGG16) is used to extract abstract discriminatory image features and this approach was due to limited burn images which made it infeasible to train a CNN model from scratch. Subsequently, decision tree, support vector machines (SVM), naïve Bayes, logistic regression, and k-nearest neighbour (KNN) are used to classify whether a given image is burnt or healthy based on the VGG16 features. The performances of these classification algorithms were extensively analysed using the VGG16 features from different layers.
Highlights
Burns are a devastating injury subjecting more than 11 million people to psychological trauma [1].These injuries cause an estimated mortality rate of over 265,000 globally per year [2,3]
Over 90% of burn incidences are in low- and middle-income countries (LMICs), about 11 times higher than the number of reported cases in high-income countries (HICs)
From FC1 and FC2 layers for classification using the decision trees (DT) algorithm are presented in the Table 1
Summary
Burns are a devastating injury subjecting more than 11 million people to psychological trauma [1] These injuries cause an estimated mortality rate of over 265,000 globally per year [2,3]. LDI has advantages over traditional methods due to its ability to asses burn wounds with no physical contact; as such. The use of CNN was recognised due to its powerful capability to automatically extract generic discriminatory features. This breakthrough technology has been applied to different generic discriminatory features. CNN models for feature extraction and support vector support vector machines for the classification of features.
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