<p>Accurate assessment of burn injuries is critical for the correct management of such wounds. Depending on the total body surface area affected by the burn, and the severity of the injury, the optimal treatment and the surgical requirements are selected. However, such assessment is considered a clinical challenge. In this thesis, to address this challenge, an automatic framework to segment the burn using RGB images, and classify the injury based on the severity using ultrasound images is proposed and implemented. With the use this framework, the conventional assessment approach, which relies exclusively on a physical and visual examination of the injury performed by medical practitioners, could be complemented and supported, yielding accurate results. The ultrasound data enables the assessment of internal structures of the body, which can provide complementary and useful information. It is a noninvasive imaging modality that provides access to internal body structures that are not visible during the typical physical examination of the burn. The semantic segmentation module of the proposed approach was evaluated through one experiment. Similarly, the classification module was evaluated through two experiments. The second experiment assessed the effects of incorporating texture features as extra features for the classification task. Experimental results and evaluation metrics demonstrated the satisfactory results obtained with the proposed framework for the segmentation and classification problem. Therefore, this work acts as a first step towards the creation of a Computer-Aided Diagnosis and Detection system for burn injury assessment.</p>

Burn injuries are a significant global health concern, causing high mortality and morbidity rates. Clinical assessment is the current standard for diagnosing burn injuries, but it suffers from interobserver variability and is not suitable for intermediate burn depths. To address these challenges, machine learning-based techniques were proposed to evaluate burn wounds in a thesis. The study utilized image-based networks to analyze two medical image databases of burn injuries from Caucasian and Black-African cohorts. The deep learning-based model, called BurnsNet, was developed and used for real-time processing, achieving high accuracy rates in discriminating between different burn depths and pressure ulcer wounds. The multiracial data representation approach was also used to address data representation bias in burn analysis, resulting in promising performance. The ML approach proved its objectivity and cost-effectiveness in assessing burn depths, providing an effective adjunct for clinical assessment. The study's findings suggest that the use of machine learning-based techniques can reduce the workflow burden for burn surgeons and significantly reduce errors in burn diagnosis. It also highlights the potential of automation to improve burn care and enhance patients' quality of life. / Petroleum Technology Development Fund (PTDF); Gombe State University study fellowship