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Journal article
MicroMPoxNet: A lightweight CNN for interpretable Monkeypox classification in dermoscopic images with comparative transfer learning evaluation
Biomedical signal processing and control, Vol.123, 110404
2026
Abstract
The recent global spread of Monkeypox (Mpox), particularly the emergence of a highly transmissible strain in 2024, poses a significant public health threat beyond endemic regions. However, timely diagnosis remains challenging, especially in resource-limited settings where PCR testing is slow and costly. Deep Learning (DL)-based skin lesion classification offers a promising diagnostic alternative, yet most existing approaches rely on computationally expensive Transfer Learning (TL) models that often lack explainability. In this study, we benchmark eleven state-of-the-art (SOTA) TL architectures under a unified protocol using both feature and parameter transfer strategies across four studies. We then propose MicroMPoxNet, a novel lightweight model integrating a Separable Convolution-based Residual Attention Network (SCRAN) with Squeeze and Excitation (SE) blocks, optimized for memory efficiency, fast inference, and clinical interpretability on edge devices. We introduce Mpox2025, a new Mpox skin lesion dataset, and evaluate all models across four studies: feature transfer (Study 0), parameter transfer with binary classification without augmentation (Study 1), with augmentation (Study 2), and multiclass classification on a public dataset (Study 3). MicroMPoxNet achieved 99.99% accuracy with an AUC of 1.00 in binary classification (Study 2) and 99.12% accuracy with an AUC of 1.00 in multiclass classification (Study 3). With only 0.63 million parameters, an 86% reduction relative to SOTA models, it achieves the lowest computational complexity. Post hoc explainability methods, including Grad CAM, LIME, and Integrated Gradients, confirm that predictions consistently focus on clinically relevant lesion regions.
Details
- Title
- MicroMPoxNet: A lightweight CNN for interpretable Monkeypox classification in dermoscopic images with comparative transfer learning evaluation
- Authors/Creators
- A. A.M. Mustahid - Shanghai University of Engineering ScienceA. A.M. Muzahid - Shanghai University of Engineering ScienceMd Sadekur Rahman - Daffodil International UniversityHua Han - Shanghai University of Engineering ScienceYujin Zhang - Shanghai University of Engineering ScienceFerdous Sohel - Murdoch University, School of Information Technology
- Publication Details
- Biomedical signal processing and control, Vol.123, 110404
- Publisher
- Elsevier Ltd.
- Number of pages
- 20
- Grant note
- 62103257 / National Natural Science Foundation of China (501100001809)
- Identifiers
- 991005882553407891
- Copyright
- © 2026 The Authors.
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Journal article
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