<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15016737 |
| Date | 20 July 2021 |
| Creators | Daniela Chanci Arrubla (11154033) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/AUTOMATIC_ASSESSMENT_OF_BURN_INJURIES_USING_ARTIFICIAL_INTELLIGENCE/15016737 |
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