Assessment of Human Skin Burns: A Deep Transfer Learning Approach

Accurate assessment of burns is increasingly sought due to diagnostic challenges faced with traditional visual assessment methods. While visual assessment is the most established means of evaluating burns globally, specialised dermatologists are not readily available in most locations and assessment is highly subjective. The use of other technical devices such as Laser Doppler Imaging is highly expensive while rate of occurrences is high in low- and middle-income countries. These necessitate the need for robust and cost-effective assessment techniques thereby acting as an affordable alternative to human expertise. In this paper, we present a technique to discriminate skin burns using deep transfer learning. This is due to deficient datasets to train a model from scratch, in which two dense and a classification layers were added to replace the existing top layers of pre-trained ResNet50 model. The proposed study was able to discriminate between burns and healthy skin in both ethnic subjects (Caucasians and Africans). We present an extensive analysis of the effect of using both homogeneous and heterogeneous datasets when training a machine learning algorithm. The findings show that using homogenous dataset during training process produces a biased diagnostic model towards minor racial subjects while using heterogeneous datasets produce a robust diagnostic model. Recognition accuracy of up to 97.1% and 99.3% using African and Caucasian datasets respectively were achieved. We concluded that it is feasible to have a robust diagnostic machine learning model for burns assessment that can be deployed to remote locations faced with access to specialized burns specialists, thereby aiding in decision-making as quick as possible