# Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models

arXiv: https://arxiv.org/abs/2506.06007 · HTML: https://mlsanity.com/paper/2506.06007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [Towards a Guideline for Evaluation Metrics in Medical Image Segmentation](https://mlsanity.com/paper/2202.05273/) (2022)
- [Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques](https://mlsanity.com/paper/2105.08446/) (2021)
- [Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data](https://mlsanity.com/paper/1509.04619/) (2015)

## Abstract (arXiv)

Orthopoxvirus infections must be accurately classified from medical pictures for an easy and early diagnosis and epidemic prevention. The necessity for automated and scalable solutions is highlighted by the fact that traditional diagnostic techniques can be time-consuming and require expert interpretation and there are few and biased data sets of the different types of Orthopox. In order to improve classification performance and lower computational costs, a hybrid strategy is put forth in this paper that uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data. The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes while preserving training and inference efficiency. The proposed approach demonstrates strong generalization and robustness across multiple evaluation settings, offering a scalable and interpretable solution for real-world clinical deployment.