![]() Second-degree burn includes superficial partial-thickness (SPT) burns and deep partial-thickness (DPT) burns. Deep burns such as second-degree and third-degree burns are distinguishable from epidermal burns by their characteristics (pain, capillary refill and colour-red/pink, white). Burns that affect epidermal layer are referred to as superficial or first-degree burns, and the common example is sunburn which can heal with no medical intervention within seven days due to proliferation and differentiation of keratinocytes from basal epithelial cells. Burns injuries are caused by several mechanisms such as thermal, electrical, radiation and chemical. However, skin injuries such burns disrupt such barrier thereby subjecting individuals to high risk of infections and in extreme cases loss of live. These skin layers, combined together, provide the aforementioned functionalities. The epidermis is the outermost layers that interface the external environment while dermis sits between epidermis and hypodermis. Skin is composed of three layers: epidermis, dermis and hypodermis. It serves as a defensive shield against foreign intruders, helps in thermoregulation, prevents loss of body fluid via evaporative, and helps significantly in the production of vitamin D. Skin is the largest body organ constituting ~ 1.5–2.0 m 2 for an average adult. ![]() ![]() The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not. The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. These shortfalls necessitate the need for objective and affordable technique. ![]() However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish.
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