EE-MpoxLiteNet: An Edge-Enhanced Lightweight and Explainable Deep Learning Framework for Monkeypox Skin Lesion Screening

Deep learning-based Monkeypox (Mpox) skin lesion
screening offers a rapid and low-cost decision-support solution
for resource-constrained healthcare settings. However, existing
approaches often depend on limited datasets, computationally
intensive architectures, and augmentation procedures that may
introduce data leakage. This paper proposes EE-MpoxLiteNet,
a lightweight, edge-enhanced, and explainable framework for
Mpox skin lesion classification. A leakage-safe protocol first
partitions the original images into training, validation, and
test subsets before augmentation. The proposed Edge-Aware
Lesion Enhancement Module combines contrast-limited adap-
tive histogram equalization, bilateral filtering, and Sobel-based
structural information to improve lesion contrast and boundary
representation. A dual-stream architecture then captures com-
plementary appearance-based semantic and edge-guided mor-
phological features. These representations are refined through
a Multi-Scale Lesion Representation Block and a Cross-Branch
Attention Fusion Module before classification. Experiments on
the Monkeypox Skin Lesion Dataset show that EE-MpoxLiteNet
achieves 97.8% accuracy, a 97.8% F1-score, and a 99.4% ROC-
AUC, outperforming standard CNN baselines and heavier hybrid
models. The network contains 6.85 million parameters and
requires only 0.86 GFLOPs, demonstrating a favorable accuracy–
efficiency trade-off. Grad-CAM visualizations further show that
predictions are primarily based on lesion-relevant regions. These
findings support the potential of EE-MpoxLiteNet for efficient
and explainable AI-assisted Mpox screening.