AI RESEARCH
Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors
arXiv CS.CV
•
ArXi:2605.05034v1 Announce Type: new Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large annotated datasets, which is an impractical requirement for emerging or rare conditions such as Monkeypox. To overcome this limitation, we propose a few-shot learning (FSL) framework that employs SimpleShot, a lightweight, non-parametric, inductive classifier, for Monkeypox and pox-like skin disease recognition from limited labeled examples.