A comparative study of deep learning architectures for classification of human burn wounds using visual light and multispectral SWIR imaging

Accurate determination of burn wound depth is crucial in the resection of injured tissue to avoid infection, complications in healing, and the unnecessary removal of healthy tissue. This study aims to lay the groundwork for improving burn depth classification accuracy through multispectral short-wave infrared (SWIR) imaging and deep learning. 267 regions of interest (ROIs) were imaged at wavelength bands ranging from 1200 to 2250 nm. Burn categories were blindly classified by surgeons using visual light images of the ROIs. When trained on the SWIR images alone, a MobileNet CNN model showed average accuracies ranging from 0.18 to 0.94 in predicting the surgeons’ classification of operable burns, superficial thickness burns, and normal skin. Classification accuracy for operable burns is significantly higher (up to 94%) compared to other classes. Ongoing work to augment accuracy includes incorporating spectral and texture-based features, histological classification, and segmentation of burns.