# Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study

arXiv: https://arxiv.org/abs/2207.03342 · HTML: https://mlsanity.com/paper/2207.03342/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)

## Built on by (most cited first, 20 of 20)

- [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)
- [Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms](https://mlsanity.com/paper/2211.15459/) (2022)
- [RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis](https://mlsanity.com/paper/2410.01216/) (2024)
- [RS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detection](https://mlsanity.com/paper/2511.15476/) (2025)
- [A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images](https://mlsanity.com/paper/2305.18489/) (2023)
- [Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere](https://mlsanity.com/paper/2308.13492/) (2023)
- [SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI](https://mlsanity.com/paper/2503.19801/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox](https://mlsanity.com/paper/2303.09780/) (2023)
- [MpoxSLDNet: A Novel CNN Model for Detecting Monkeypox Lesions and Performance Comparison with Pre-trained Models](https://mlsanity.com/paper/2405.21016/) (2024)
- [A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking](https://mlsanity.com/paper/2505.19023/) (2025)
- [RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Images](https://mlsanity.com/paper/2601.01835/) (2026)
- [Adversarial Masked Image Inpainting for Robust Detection of Mpox and Non-Mpox](https://mlsanity.com/paper/2310.06318/) (2023)
- [Monkeypox disease recognition model based on improved SE-InceptionV3](https://mlsanity.com/paper/2403.10087/) (2024)
- [Adapting an Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool for Mpox Detection: The HeHealth Experience](https://mlsanity.com/paper/2404.16885/) (2024)
- [Attention Based Feature Fusion Network for Monkeypox Skin Lesion Detection](https://mlsanity.com/paper/2408.06640/) (2024)
- [MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection](https://mlsanity.com/paper/2409.04218/) (2024)
- [Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis](https://mlsanity.com/paper/2505.01429/) (2025)
- [Smart Waste Management System for Makkah City using Artificial Intelligence and Internet of Things](https://mlsanity.com/paper/2505.19040/) (2025)
- [Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors](https://mlsanity.com/paper/2605.05034/) (2026)

## Abstract (arXiv)

The recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Clinical diagnosis of monkeypox in an early stage is challenging due to its similarity with chickenpox and measles. In cases where the confirmatory Polymerase Chain Reaction (PCR) tests are not readily available, computer-assisted detection of monkeypox lesions could be beneficial for surveillance and rapid identification of suspected cases. Deep learning methods have been found effective in the automated detection of skin lesions, provided that sufficient training examples are available. However, as of now, such datasets are not available for the monkeypox disease. In the current study, we first develop the ``Monkeypox Skin Lesion Dataset (MSLD)" consisting skin lesion images of monkeypox, chickenpox, and measles. The images are mainly collected from websites, news portals, and publicly accessible case reports. Data augmentation is used to increase the sample size, and a 3-fold cross-validation experiment is set up. In the next step, several pre-trained deep learning models, namely, VGG-16, ResNet50, and InceptionV3 are employed to classify monkeypox and other diseases. An ensemble of the three models is also developed. ResNet50 achieves the best overall accuracy of 82.96(±4.57%), while VGG16 and the ensemble system achieved accuracies of 81.48(±6.87%) and 79.26(±1.05%), respectively. A prototype web-application is also developed as an online monkeypox screening tool. While the initial results on this limited dataset are promising, a larger demographically diverse dataset is required to further enhance the generalizability of these models.