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

TL;DR

This paper proposes a hybrid machine learning and deep learning approach for classifying Orthopoxvirus images, aiming to improve accuracy and efficiency for early diagnosis and epidemic control.

Contribution

It introduces a novel hybrid strategy combining ML and pretrained DL models for feature extraction without data augmentation, enhancing classification performance and scalability.

Findings

Achieved high classification accuracy with the hybrid approach

Demonstrated robustness across multiple evaluation settings

Reduced computational costs compared to traditional methods

Abstract

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.