# Monkeypox virus detection using pre-trained deep learning-based approaches

arXiv: https://arxiv.org/abs/2209.04444 · HTML: https://mlsanity.com/paper/2209.04444/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16](https://mlsanity.com/paper/2206.01862/) (2022)
- [Monkeypox Image Data collection](https://mlsanity.com/paper/2206.01774/) (2022)
- [New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis](https://mlsanity.com/paper/2012.15413/) (2020)
- [Accurate Measles Rash Detection via Vision Transformer Fine-Tuning](https://mlsanity.com/paper/2005.09112/) (2020)

## Built on by (most cited first, 14 of 14)

- [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)
- [AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments](https://mlsanity.com/paper/2211.14313/) (2022)
- [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)
- [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)
- [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)
- [Adapting an Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool for Mpox Detection: The HeHealth Experience](https://mlsanity.com/paper/2404.16885/) (2024)
- [Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis](https://mlsanity.com/paper/2505.01429/) (2025)
- [Comparative Performance of Finetuned ImageNet Pre-trained Models for Electronic Component Classification](https://mlsanity.com/paper/2506.19330/) (2025)

## Abstract (arXiv)

Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.