Feature Fusion of Ensemble Deep Learning Models for Classification of Monkeypox Disease

Abstract

Outbreaks of human monkeypox have been reported in many nations recently, including India. Studies and reports indicate that to reduce the spread of the infection, it is crucial to identify and isolate affected individuals as soon as possible. The purpose of this work is to use integration of pre-trained deep learning models for extracting the features from monkeypox image dataset and then combine them for detecting and diagnosing Monkeypox disease. Combining features provides a rich and comprehensive representation of the input data, enabling the classifier to make more reliable predictions. In our study we used 5 feature fusion techniques to obtain and select best feature representations. Each method offers different ways of fusing information.  Two publicly available datasets are utilised: Monkeypox Skin Image Dataset (MSID), Monkeypox Skin Lesion Detection (MSLD). Fusing features from models like pre-trained models effectively combines their strengths, improving generalization, accuracy, and robustness in Monkeypox detection. The model's performance was evaluated using six metrics: accuracy, recall, precision, F1-score, ROC, and AUC. Our experimental results demonstrate that the combined features from pre-trained DenseNet121 and MobileNetV2 models using multiplicative fusion method have the best classification accuracy of 95.56% on MSLD and 91.67% on MSID.