# Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination
## 1 Introduction  
Burns are injuries affecting both elderly, young and children, causing loss of lives and subjecting both families and nations to additional economic burden. An estimated sum of $7.5 billion has been reported as the annual expenditure for the management of burn injuries in America [1], third most devastating cause of unintentional injuries and second most serious injury affecting elderly people. Another report by Reference [2] shows that burn is the most common injury affecting children below the age of five in England, causing about 7000 admissions in hospitals each year. Many research reports [3; 4; 5] have lamented a worrisome situation in poor countries where high cases (up to 90%) of burns injuries are recorded with high percentage rate of mortality and was attributed to lack of social awareness programs related to prevention and safety measures, while most of the cost-effective and safety measures are practically implemented in high income countries [1]. In general, studies [6; 7] have shown that about 265,000 deaths are reported every year, as such burns have become eminent health concern globally.  
Burns heal within a three-week window period though superficial burn heals in 5-7 days post-burn. Effective and early assessment is important in order to determine whether a conservative treatment or surgical excision is required to avoid further complications. Assessment of burns is effective clinicallywhen the burn injuries involves superficial or full-thickness and requires experienced dermatologist or surgeons and time consuming [1]. Overall, the assessment of burns by experienced clinicians is accurate in only between 65-75% [8; 9]. For this reason, certain number of modalities were introduced to aid clinicians in making accurate evaluation of the burn injuries. Laser Doppler Imaging (LDI) is one of the examples of the introduced modalities that received unanimous acceptance and approved by burn community. LDI operates based on Doppler effect principle and assesses burn depth by providing healing potentials of the wounds. The advantages of using LDI includes its non-invasive nature and effectiveness than human experts. However, LDI has some shortfall such as high affordability cost, assessment ineffectiveness in first 24 h after burn [10] and not suitable for pediatric patients due to movement artifacts.  
Interestingly, the use of machine learning techniques has been penetrating different sectors including healthcare to boost operational efficiency and help minimize inconsistencies due to human errors. Therefore, in this paper we proposed the use of machine learning to discriminate burns and injured skin. The motivation behind this proposal are--(1) human expertise is not readily available in most remote locations; (2) assessment by human experts is time consuming and subjective [11]; (3) up to-date approved device (i.e., LDI) for burns assessment by burn community is highly expensive [12] and (4) to the best of our knowledge, no technique was proposed to address discriminating burns and injured skin, as such we proposed the use of machine learning to discriminate whether a given image is burn or injured. The use of machine learning can serve as alternative to human expertise thereby aiding in timely decision making. Transfer learning technique was used due to deficient datasets to train a model from scratch. The rest of the paper is organized as follows--Section 2 provides a review related literature; Section 3 presents materials used and the methodology; Section 4 presents results and discussion; while in Section 5 conclusion was presented.