A novel optimized deep ensemble for monkeypox lesion detection using transfer learning and ABC algorithm

Abstract

Monkeypox, a reemerging zoonotic disease, has become a serious global health concern that requires rapid and accurate diagnostic methods. The increasing prevalence of skin lesion images highlights the scope of automated deep learning solutions. However, current approaches face challenges such as limited generalization and poor performance on imbalanced datasets. This paper aims to develop a robust diagnostic framework by addressing these limitations through a novel ensemble learning strategy. We propose MPox-Net1.0: An Artificial Bee Colony (ABC)-optimized weighted ensemble of pre-trained convolutional neural networks (CNNs) for monkeypox classification. The methodology integrates transfer learning with swarm intelligence to dynamically balance model contributions based on prediction confidence. Eight state-of-the-art CNNs were fine-tuned on the MSLD v2.0 dataset, with Xception, EfficientNetB3, and EfficientNetB0 selected for the ensemble based on performance. The proposed ABC-weighted ensemble achieved an accuracy of 99.12%, improved by 0.66% over the best individual model. It also achieved better precision (99.15%), recall (99.12%), and F1 score (99.12%) compared to existing ensemble methods. Overall, our technique demonstrates enhanced minority class recognition and computational efficiency, establishing its potential for clinical deployment.