Development of a Hybrid Monkeypox Detection Model Using Cnn and Extreme Gradient Boosting

The Monkeypox virus poses as a public health risk that might quickly escalate into a worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, ï¬ nding tumor cells, and ï¬ nding COVID-19 patients. The timely identification and accurate categorization of Monkeypox cutaneous manifestations are crucial for the successful implementation of containment strategies. It requires sophisticated methodologies to detect and combat this evolving orthopoxvirus at an early stage. This study presents an exploration of a hybrid machine learning model integrating CNN (Convolutional Neural Network), XGBoost (Extreme Gradient Boosting), and XGboost based stack model for classification and detection with other standard machine learning methods such as Support Vector Machine (SVM) and Random Forest, where transfer learning and the DL algorithms for the skin lesion data will enhance and train model, while SHAP methods were adopted to examine and analyze XGBoost predictions. The resulting ensemble model is not only adept at detecting Monkeypox virus through its lesion and symptoms but also showcases computational efficiency with a predictive accuracy, recall, precision, and F1 Score, all reaching a value of 1.0. In a comparison analysis conducted on other deep learning models, the suggested model has superior performance as a hybrid model compared to other models. The exceptional performance demonstrated in this study underscores the effectiveness of the methodology in accurately classifying skin lesions and symptoms linked to Monkeypox. This approach holds promise for individuals, as it enables early detection, a vital factor in preventing the spread of Monkeypox.