Metaheuristics optimization-based ensemble of deep neural networks for Mpox disease detection.

The rising number of cases of human Mpox has emerged as a major global concern due to the daily increase of cases in several countries. The disease presents various skin symptoms in infected individuals, making it crucial to promptly identify and isolate them to prevent widespread community transmission. Rapid determination and isolation of infected individuals are therefore essential to curb the spread of the disease. Most research in the detection of Mpox disease has utilized convolutional neural network (CNN) models and ensemble methods. However, to the best of our knowledge, none have utilized a meta-heuristic-based ensemble approach. To address this gap, we propose a novel metaheuristics optimization-based weighted average ensemble model (MO-WAE) for detecting Mpox disease. We first train three transfer learning (TL)-based CNNs (DenseNet201, MobileNet, and DenseNet169) by adding additional layers to improve their classification strength. Next, we use a weighted average ensemble technique to fuse the predictions from each individual model, and the particle swarm optimization (PSO) algorithm is utilized to assign optimized weights to each model during the ensembling process. By using this approach, we obtain more accurate predictions than individual models. To gain a better understanding of the regions indicating the onset of Mpox, we performed a Gradient Class Activation Mapping (Grad-CAM) analysis to explain our model's predictions. Our proposed MO-WAE ensemble model was evaluated on a publicly available Mpox dataset and achieved an impressive accuracy of 97.78%. This outperforms state-of-the-art (SOTA) methods on the same dataset, thereby providing further evidence of the efficacy of our proposed model.