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

2025-06-06

AI Summary

To address the scarcity of clinical dermatological imagery for orthopoxviruses, substantial annotation bias, and heavy reliance on expert interpretation, this paper proposes a lightweight hybrid feature extraction framework that requires neither data augmentation nor model fine-tuning. The method integrates deep visual features extracted by pretrained CNNs (e.g., ResNet, VGG) with conventional classifiers—including SVM and random forests—to establish an interpretable, deployable two-stage classification paradigm. Its key innovation lies in the first-of-its-kind synergistic mechanism between pretrained features and shallow classifiers, markedly enhancing generalization and robustness under few-shot conditions. Evaluated on a multi-source orthopoxvirus image dataset, the framework achieves 98.2% classification accuracy and accelerates inference speed by 3.1× over pure deep learning baselines, enabling real-time clinical decision support.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Accurate classification of Orthopoxvirus infections from medical images
• Overcoming limitations of time-consuming traditional diagnostic techniques
• Addressing scarce and biased Orthopox datasets for automated diagnosis

Innovation

• Hybrid Machine Learning and Deep Learning models
• Pretrained Deep Learning for feature extraction
• Scalable and interpretable clinical solution