Skin Lesion Classification in Low-Resource Settings Using Lightweight CNNs with Uncertainty Estimation
A computationally efficient skin lesion classification framework for seven classes using EfficientNet-B0, complemented by Monte Carlo (MC) Dropout for uncertainty quantification is introduced, and the only lightweight method in the comparison providing calibrated uncertainty estimates.