---
title: Monkeypox Skin Lesion Dataset
url: https://www.emergentmind.com/topics/monkeypox-skin-lesion-dataset
type: topic
---

# Monkeypox Skin Lesion Dataset

The Monkeypox Skin Lesion Dataset (MSLD) is an openly accessible skin-lesion image dataset introduced in 2022 to support computer-aided screening of monkeypox from lesion photographs when confirmatory Polymerase Chain Reaction (PCR) testing is not readily available [2207.03342]. Although the source imagery includes monkeypox, chickenpox, and measles cases, the original task formulation is explicitly binary: **Monkeypox** versus **Others**, with the latter combining chickenpox and measles because of their visual similarity to monkeypox lesions [2207.03342]. MSLD became one of the earliest public benchmarks for transfer-learning-based mpox lesion recognition and was subsequently reused in studies on lightweight CNNs, explainable AI, edge deployment, and few-shot learning [2405.21016][2507.17123][2605.05034].

## 1. Origin and intended scope

MSLD was created because, at the time of the original study, **there was no publicly available monkeypox skin-lesion dataset** suitable for developing automated detection methods [2207.03342]. The motivating clinical problem was early differential diagnosis: monkeypox lesions can resemble **chickenpox and measles**, and this similarity complicates rapid recognition, especially in settings where PCR confirmation is inaccessible or delayed [2207.03342].

The dataset was intended for **binary classification**, **screening support**, and **transfer-learning research** on a small medical image corpus [2207.03342]. In the original feasibility study, the authors positioned MSLD as a basis for **early identification of suspected monkeypox cases** and for eventual deployment in a **web-based screening tool** [2207.03342].

This framing is important because MSLD was never presented as a definitive diagnostic resource. Its role was preliminary and infrastructural: to establish whether transfer learning on lesion photographs could support surveillance and rapid triage during an outbreak. A plausible implication is that MSLD should be interpreted primarily as an early benchmark for feasibility studies rather than as a mature clinical dataset.

## 2. Acquisition, verification, and curation pipeline

MSLD was **manually compiled** from **publicly available case reports**, **news portals**, and **websites**, and the paper explicitly states that the authors **did not use automatic web-scrapers** [2207.03342]. Instead, the collection process relied on **extensive manual searching** in order to preserve image quality and label reliability [2207.03342]. The retained images came from patients with lesions on body regions including the **face**, **neck**, **hand**, **arm**, and **leg** [2207.03342].

To improve reliability, all images were checked using **Google Reverse Image Search** and were **cross-referenced with other sources** [2207.03342]. Quality control then proceeded through a **2-stage screening process** that removed **out-of-focus images**, **low-resolution images**, and **low-quality images**; only **unique images** that satisfied the quality criteria were retained [2207.03342]. The selected images were then **cropped to the region of interest**, **resized to 224 × 224 pixels**, and processed while maintaining aspect ratio [2207.03342]. The dataset was made openly accessible through the project **GitHub repository** mentioned in the paper [2207.03342].

These design choices distinguish MSLD from a raw web scrape. The dataset is still web-sourced, but its curation pipeline includes source triangulation, duplicate control, quality filtering, and lesion-focused preprocessing. That combination made it unusually usable for CNN experimentation relative to more heterogeneous outbreak-era image collections.

## 3. Class structure, scale, and augmentation regime

MSLD contains **two labels**: **Monkeypox** and **Others**, where **Others** combines **chickenpox and measles** [2207.03342]. The original dataset contains **228** images from **162** unique patients: **102** monkeypox images from **55** patients and **126** others images from **107** patients [2207.03342].

| Class label | Original images | Augmented images |
|---|---:|---:|
| Monkeypox | 102 | 1428 |
| Others | 126 | 1764 |
| Total | 228 | 3192 |

Because monkeypox images were scarce, the dataset was expanded by about **14-fold** through augmentation [2207.03342]. The augmentation methods listed in the paper are **rotation**, **translation**, **reflection**, **shear**, **hue jitter**, **saturation jitter**, **contrast jitter**, **brightness jitter**, **noise**, and **scaling** [2207.03342]. A figure in the paper further illustrates **13 augmented versions** of one original monkeypox image, including **random rotation by a multiple of 90°**, **random rotation in the range of −45° to 45°**, **translation**, **reflection**, **shear**, **hue jitter**, **saturation jitter**, **brightness jitter**, **contrast jitter**, **salt-and-pepper noise**, **Gaussian noise**, **synthetic blur**, and **scaling** [2207.03342].

The resulting augmented corpus contains **1,428** monkeypox images and **1,764** others images, for a total of **3,192** images [2207.03342]. The original dataset is therefore **slightly imbalanced**, both before and after augmentation, in favor of the **Others** class [2207.03342].

A common misunderstanding is that MSLD is intrinsically a large dataset. It is not. The large image count is augmentation-derived; the original evidence base remains **228 images** from **162** patients. This distinction is central to interpreting both model performance and generalization claims.

## 4. Original experimental protocol and baseline benchmarking

The original feasibility study evaluated MSLD with **3-fold cross-validation** and a split into **training**, **validation**, and **test** sets with an approximate ratio of **70:10:20**, while maintaining **patient independence** [2207.03342]. An important design decision was that **only the training and validation sets were augmented**, whereas the **test set contained only original images** [2207.03342]. This prevented test-time evaluation on synthetic samples.

The study benchmarked three ImageNet-pretrained CNNs—**VGG-16**, **ResNet50**, and **InceptionV3**—together with an **ensemble model** based on **majority voting** [2207.03342]. The input size was **224 × 224 × 3**; the pretrained fully connected layers were removed; the bottom **8 layers** were unfrozen; and a new classifier head was added with **FC 4096**, **FC 1072**, **FC 256**, and a final **FC layer with 2 outputs + softmax** [2207.03342]. The reported dropout rates were **0.3**, **0.2**, and **0.15**; the optimizer was **Adam** with learning rate **1e−5**; batch size was **16**; and the loss was **binary cross-entropy** [2207.03342].

The reported 3-fold results were as follows: **VGG16** achieved **81.48 ± 6.87** accuracy, **0.85 ± 0.08** precision, **0.81 ± 0.05** recall, and **0.83 ± 0.06** F1; **ResNet50** achieved **82.96 ± 4.57** accuracy, **0.87 ± 0.07** precision, **0.83 ± 0.02** recall, and **0.84 ± 0.03** F1; **InceptionV3** achieved **74.07 ± 3.78** accuracy, **0.74 ± 0.02** precision, **0.81 ± 0.07** recall, and **0.78 ± 0.04** F1; and the **ensemble** achieved **79.26 ± 1.05** accuracy, **0.84 ± 0.05** precision, **0.79 ± 0.07** recall, and **0.81 ± 0.02** F1 [2207.03342]. **ResNet50** was therefore the best overall model in the original benchmark [2207.03342]. The same study also produced a **prototype web-application** as an online monkeypox screening tool [2207.03342].

These numbers established MSLD as a feasibility benchmark rather than a saturated one. The gap between **ResNet50** and **InceptionV3**, and the fact that the ensemble did not surpass the best single model, indicate that representation choice mattered strongly under low-data conditions.

## 5. Subsequent reuse as a benchmark

Later work reused MSLD in several distinct methodological settings. **MpoxSLDNet** treated the augmented MSLD as a binary benchmark of **3,192** images, used a **90% training / 10% testing/validation** split, and reported **94.56%** validation/testing accuracy for a lightweight custom CNN, compared with **86.25%** for **VGG16**, **84.38%** for **DenseNet121**, and **67.19%** for **ResNet50** under that paper’s protocol [2405.21016]. An **Attention Based Feature Fusion Network** later used the augmented Kaggle MSLD with **4-fold cross-validation** and reported **96.52%** mean validation accuracy, **96.58%** precision, **96.52%** recall, and **96.51%** F1-score [2408.06640].

MSLD was also adopted in deployment-oriented studies. An embedded-system study trained **MobileNetV2** on the open-source MSLD for binary classification on the **NVIDIA Jetson Orin Nano** and reported **92.19%** accuracy, **91.30%** precision, **94.92%** recall, and **93.07%** F1-score [2507.17123]. That same paper noted an important failure mode: on a **320-image** test set, **20** images were misclassified and **15** of those errors were monkeypox images mislabeled as **Others**, highlighting a false-negative risk in screening applications [2507.17123].

The dataset has additionally served as a protocol stress test. The **ITMAINN** study used the Kaggle version of the binary MSLD alongside **stratified 80:20 train-test split** and **stratified 5-fold cross-validation**, again emphasizing that augmentation was applied **only to the training data** [2505.19023]. A later few-shot benchmark used the **original, unaugmented images only** from **MSLD v1.0**, defining it as a **2-way benchmark** with **1-shot**, **5-shot**, and **10-shot** episodes [2605.05034].

Taken together, these studies show that MSLD has functioned less as a single fixed benchmark than as a reusable binary corpus for transfer learning, architecture comparison, compression, embedded deployment, explainability, and few-shot representation testing. This suggests that reported accuracies on MSLD should be interpreted in light of the specific split policy, augmentation regime, and evaluation protocol used by each study.

## 6. Limitations, misconceptions, and relation to later datasets

The original paper identifies several limitations: a **small number of unique patients**; limited demographic diversity in **geography**, **race**, and **gender**; absence of richer metadata such as **clinical history**, **days since symptom onset**, and **lesion stage**; labels based on **web-sourced material** rather than prospectively collected clinical data; and pretraining on **ImageNet**, which does not contain skin-lesion images [2207.03342]. The authors recommend a **larger, more diverse dataset**, improved demographic coverage, inclusion of clinical metadata, use of a **multi-source dermatoscopic pretraining dataset**, and expansion through **international collaboration** [2207.03342].

A recurrent misconception is to conflate the original MSLD with its later follow-up. The original MSLD is a **binary** dataset with **Monkeypox** and **Others**. The expanded **MSLD v2.0** is a distinct **six-class** dataset containing **mpox**, **chickenpox**, **measles**, **cowpox**, **hand-foot-mouth disease (HFMD)**, and **healthy**, with **755 images** from **541 distinct patients** and dermatologist verification of disease labels [2306.14169]. MSLD v2.0 was created precisely to address limitations in the earlier release, including limited class coverage, image quality issues, and skin-tone representation [2306.14169].

The broader literature has treated these limitations as characteristic of the field rather than of MSLD alone. A 2023 survey describes the mpox skin-lesion dataset ecosystem as constrained by **limited, non-standardized, and often small-scale datasets**, with recurring issues of class imbalance, label uncertainty, duplication risk, and limited external validity [2311.10754]. Within that landscape, MSLD remains historically important because it filled an early public-data gap and established a concrete benchmark for lesion-based monkeypox screening. Its lasting significance lies less in its size than in its role as a catalyst for subsequent dataset releases, protocol refinements, and methodological comparisons across the mpox computer-vision literature.

Source: https://www.emergentmind.com/topics/monkeypox-skin-lesion-dataset