Deep Learning based fully automatic efficient Burn Severity Estimators for better Burn Diagnosis

Each year, burn injuries lead to several deaths and lifelong disabilities for many others. Timely provided appropriate diagnosis and treatment can reduce sufferings for many, however automated burns diagnosis techniques are still under exploration. Laser Doppler Imaging (LDI) has been found as promising for burns depth assessment, but high costs, delays and portability issues limit its usage in developing automated burns diagnosis methods. The visual images based automated approaches for burn diagnosis have been limitedly explored. This research presents a deep learning based novel approach for burn severity assessment and a new labeled dataset of burn images with varying burn severity that would be made publically available in order to facilitate and advance research for burn severity estimation. As skin characteristics vary across different body regions so will be the burn impact, so we propose customized burn severity estimators (specific to body parts) instead of having a single generic burn severity estimator for the whole human body. Extensive experiments were conducted to evaluate the performance of the proposed approach with different network settings, obtaining competitive results to state-of-the-art methods, despite each customized estimator using a smaller set of images compared to generic one. Also, the experiments suggest that the deep learning based customized estimators perform better than handcrafted features based methods for burns diagnosis.