Monkeypox Detection and Classification Using Deep Learning With Ensemble Method and Attention Mechanisms

As the world adjusts to the current SARS-CoV-2 epidemic, another zoonotic disease caused by a monkeypox virus has emerged across the world poses a major public health issues, demanding the use of precise and efficient diagnostic methods for detection and classification. In this paper, we offer a new deep learning framework that combines ensemble modeling with mechanisms of attention to improve monkeypox disease detection and classification. We extracted discriminative features from Monkeypox Skin Lesion Dataset version 2.0 using ten pre-trained convolutional neural network models enhanced with attention mechanisms. Later we combined the best models into ensemble model for further improvement of model performance. We proposed a majority voting ensemble model of ResNet50V2, DenseNet121, DenseNet201 where each standalone model is modified with Convolutional Block Attention Mechanism. Our approach produced remarkable results with an accuracy of 94.43% for multiclass and 96.95% for binary classification which proving our proposed model’s robustness and effectiveness in identifying monkeypox skin lesions. We used explainable artificial intelligence approaches such as Grad-CAM, SHAP, and LIME to ensure transparency and interpretability. These methods provided insights into the model’s decision-making process by emphasizing significant locations and features that contributed to the predictions. This study not only advances the field of automated monkeypox diagnosis, but it also emphasizes the need of using attention processes and ensemble strategy to improve classification performance while preserving model interpretability.