Determining the degree of skin burns is difficult for both general practitioners and specialists. This study aimed to utilize AI models in the classification of skin burns using high-quality datasets. Experts first divided skin burns into 4 classes (I, IIa, IIb, III). These images were then preprocessed using ROI extraction, conversion to the CIELAB colour space, and image enhancement to improve quality and reduce noise. These preprocessed images, with their clinical classifications, were used to train the deep learning models ResNet50, DenseNet, MobileNet, VGG16, and ShuffleNet, to learn patterns associated with each class. The models' performance was then tested on new images. Two approaches were compared: multiclass classification (distinguish between all four classes) and binary classification (distinguish between Class I vs others and Class II vs Class III). The binary classification (accuracy 94.03%, F1 score 0.9384), performed better than the multiclass classification (accuracy 63.23%, F1-score of 0.6271).