Abstract:Purpose
Precise evaluation of burn depth is essential for determining the appropriate patient care and surgical requirements. This study aimed to examine a supervised machine learning approach that incorporates dynamic feature selection for differentiating between partial-thickness and full-thickness burns, utilizing deep learning patterns in digital images.
Method
Four deep learning models (VGG-16, ResNet-50, Xception, and EfficientNetV2L), along with two classifiers (Support Vector Machine and Fully Connec… Show more
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