# Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

arXiv: https://arxiv.org/abs/2605.05034 · HTML: https://mlsanity.com/paper/2605.05034/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 55 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning](https://mlsanity.com/paper/1911.04623/) (2019)
- [A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2205.06743/) (2022)
- [Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study](https://mlsanity.com/paper/2207.03342/) (2022)
- [A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity](https://mlsanity.com/paper/2306.14169/) (2023)
- [Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification](https://mlsanity.com/paper/2109.02394/) (2021)
- [Two Decades of Bengali Handwritten Digit Recognition: A Survey](https://mlsanity.com/paper/2206.02234/) (2022)
- [Rethinking Cooking State Recognition with Vision Transformers](https://mlsanity.com/paper/2212.08586/) (2022)
- [AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions](https://mlsanity.com/paper/2506.17455/) (2025)
- [Performance Evaluation of Large Language Models in Bangla Consumer Health Query Summarization](https://mlsanity.com/paper/2505.05070/) (2025)
- [HaSPeR: An Image Repository for Hand Shadow Puppet Recognition](https://mlsanity.com/paper/2408.10360/) (2024)
- [Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms](https://mlsanity.com/paper/2211.15459/) (2022)
- [AttResDU-Net: Medical Image Segmentation Using Attention-based Residual Double U-Net](https://mlsanity.com/paper/2306.14255/) (2023)
- [Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition](https://mlsanity.com/paper/2506.00447/) (2025)
- [DExNet: Combining Observations of Domain Adapted Critics for Leaf Disease Classification with Limited Data](https://mlsanity.com/paper/2506.18173/) (2025)
- [Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI](https://mlsanity.com/paper/2410.07260/) (2024)
- [Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model](https://mlsanity.com/paper/2505.24007/) (2025)
- [Cross-Domain Evaluation of Few-Shot Classification Models: Natural Images vs. Histopathological Images](https://mlsanity.com/paper/2410.09176/) (2024)

## Abstract (arXiv)

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. The proposed pipeline passes the skin lesion images through a frozen, pretrained CNN backbone to obtain feature embeddings, which are then classified via SimpleShot using nearest-centroid comparisons in a normalized embedding space. We systematically benchmark six widely used CNN backbones as feature extractors under consistent experimental settings, enabling fair comparison. Experiments on three publicly available datasets (MSLD v1.0, MSID, and MSLD v2.0) are conducted across 2-way, 4-way, and 6-way tasks with 1-shot, 5-shot, and 10-shot configurations. Among all models, MobileNetV2_100 consistently achieves the highest accuracy. In addition, we present a cross-dataset evaluation for Monkeypox classification, revealing that binary Mpox-vs-Others transfer remains comparatively stable while multi-class performance degrades significantly under domain shift. Together, these results demonstrate the practical utility of combining inductive FSL methods with lightweight CNN backbones and highlight the importance of domain robustness for reliable real-world clinical deployment.