Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.

Abstract

The daily surge in cases in many nations has made the growing number of human monkeypox (Mpox) cases an important global concern. Therefore, it is imperative to identify Mpox early to prevent its spread. The majority of studies on Mpox identification have utilized deep learning (DL) models. However, research on developing a reliable method for accurately detecting Mpox in its early stages is still lacking. This study proposes an ensemble model composed of three improved DL models to more accurately classify Mpox in its early phases. We used the widely recognized Mpox Skin Images Dataset (MSID), which includes 770 images. The enhanced Swin Transformer (SwinViT), the proposed ensemble model Mpox-XDE, and three modified DL models-Xception, DenseNet201, and EfficientNetB7-were used. To generate the ensemble model, the three DL models were combined via a Softmax layer, a dense layer, a flattened layer, and a 65% dropout. Four neurons in the final layer classify the dataset into four categories: chickenpox, measles, normal, and Mpox. Lastly, a global average pooling layer is implemented to classify the actual class. The Mpox-XDE model performed exceptionally well, achieving testing accuracy, precision, recall, and F1-score of 98.70%, 98.90%, 98.80%, and 98.80%, respectively. Finally, the popular explainable artificial intelligence (XAI) technique, Gradient-weighted Class Activation Mapping (Grad-CAM), was applied to the convolutional layer of the Mpox-XDE model to generate overlaid areas that effectively highlight each illness class in the dataset. This proposed methodology will aid professionals in diagnosing Mpox early in a patient's condition.