# Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights

arXiv: https://arxiv.org/abs/2211.05633 · HTML: https://mlsanity.com/paper/2211.05633/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (2021)
- [Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes](https://mlsanity.com/paper/1711.04325/) (2017)
- [Machine Learning-Based Heart Disease Diagnosis: A Systematic Literature Review](https://mlsanity.com/paper/2112.06459/) (2021)
- [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)

## Built on by (most cited first, 3 of 3)

- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2304.10582/) (2023)
- [Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches](https://mlsanity.com/paper/2310.08645/) (2023)
- [Explainable AI-Driven Detection of Human Monkeypox Using Deep Learning and Vision Transformers: A Comprehensive Analysis](https://mlsanity.com/paper/2505.01429/) (2025)

## Abstract (arXiv)

The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) based disease prediction has demonstrated true potential by providing early, cheap, and affordable diagnosis facilities. Considering this opportunity, we have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%. Our findings are reinforced by recent academic work that demonstrates improved performance in constructing multiple disease diagnosis models using transfer learning approaches. Lastly, we further explain our model prediction using Local Interpretable Model-Agnostic Explanations (LIME), which play an essential role in identifying important features that characterize the onset of Monkeypox disease.