An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm

2025-04-24

AI Summary

To address the clinical need for early, accurate differential diagnosis of monkeypox, this paper proposes an interpretable end-to-end framework for skin lesion image classification, distinguishing monkeypox from varicella and measles. Methodologically, it innovatively integrates Xception-based transfer learning with PCA for discriminative feature extraction, introduces the African Vulture Optimization Algorithm (AVOA) to optimize an NGBoost probabilistic classifier—marking its first application in this domain—and employs dual-path interpretability via Grad-CAM and LIME for decision visualization. Evaluated on the MSLD dataset, the model achieves 97.53% accuracy, 97.72% F1-score, and 97.47% AUC, substantially outperforming existing approaches. The primary contributions are: (i) establishing the first interpretable deep learning paradigm specifically designed for monkeypox differential diagnosis; and (ii) achieving state-of-the-art performance with high accuracy, robustness, and clinically meaningful interpretability.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Security and Privacy: Data transparency and provenance

Abstract

The recent global spread of monkeypox, particularly in regions where it has not historically been prevalent, has raised significant public health concerns. Early and accurate diagnosis is critical for effective disease management and control. In response, this study proposes a novel deep learning-based framework for the automated detection of monkeypox from skin lesion images, leveraging the power of transfer learning, dimensionality reduction, and advanced machine learning techniques. We utilize the newly developed Monkeypox Skin Lesion Dataset (MSLD), which includes images of monkeypox, chickenpox, and measles, to train and evaluate our models. The proposed framework employs the Xception architecture for deep feature extraction, followed by Principal Component Analysis (PCA) for dimensionality reduction, and the Natural Gradient Boosting (NGBoost) algorithm for classification. To optimize the model's performance and generalization, we introduce the African Vultures Optimization Algorithm (AVOA) for hyperparameter tuning, ensuring efficient exploration of the parameter space. Our results demonstrate that the proposed AVOA-NGBoost model achieves state-of-the-art performance, with an accuracy of 97.53%, F1-score of 97.72% and an AUC of 97.47%. Additionally, we enhance model interpretability using Grad-CAM and LIME techniques, providing insights into the decision-making process and highlighting key features influencing classification. This framework offers a highly precise and efficient diagnostic tool, potentially aiding healthcare providers in early detection and diagnosis, particularly in resource-constrained environments.

Problem

• Automated monkeypox detection from skin lesion images
• Optimizing model performance with AVOA hyperparameter tuning
• Enhancing interpretability using Grad-CAM and LIME techniques

Innovation

• Xception for deep feature extraction
• NGBoost with PCA for classification
• AVOA optimizes hyperparameter tuning