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.

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