CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection

• Published in: Neural networks Vol. 173; p. 106183
• Main Authors: Asif, Sohaib, Zhao, Ming, Li, Yangfan, Tang, Fengxiao, Zhu, Yusen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2024
• Subjects: Chaos game optimization algorithm; Deep learning; Ensemble methods; Medical image classification; Mpox disease detection; Mpox disease detection; Deep learning; Ensemble methods; Chaos game optimization algorithm; Medical image classification
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2024.106183

•Our ensemble combines five transfer learning-based models to enhance disease detection capabilities.•We enhance the base models by incorporating feature integration layers and residual blocks, allowing them to extract intricate features and patterns effectively.•We employ the chaos game optimization algorithm to optimize base model weights efficiently, resulting in improved ensemble performance.•Our evaluation, conducted on both benchmark and newly curated datasets, highlights CGO's superiority in terms of accuracy and ensemble effectiveness.•Our approach presents a robust solution for the detection of monkeypox from skin images, significantly enhancing accuracy in disease diagnosis. The rising global incidence of human Mpox cases necessitates prompt and accurate identification for effective disease control. Previous studies have predominantly delved into traditional ensemble methods for detection, we introduce a novel approach by leveraging a metaheuristic-based ensemble framework. In this research, we present an innovative CGO-Ensemble framework designed to elevate the accuracy of detecting Mpox infection in patients. Initially, we employ five transfer learning base models that integrate feature integration layers and residual blocks. These components play a crucial role in capturing significant features from the skin images, thereby enhancing the models' efficacy. In the next step, we employ a weighted averaging scheme to consolidate predictions generated by distinct models. To achieve the optimal allocation of weights for each base model in the ensemble process, we leverage the Chaos Game Optimization (CGO) algorithm. This strategic weight assignment enhances classification outcomes considerably, surpassing the performance of randomly assigned weights. Implementing this approach yields notably enhanced prediction accuracy compared to using individual models. We evaluate the effectiveness of our proposed approach through comprehensive experiments conducted on two widely recognized benchmark datasets: the Mpox Skin Lesion Dataset (MSLD) and the Mpox Skin Image Dataset (MSID). To gain insights into the decision-making process of the base models, we have performed Gradient Class Activation Mapping (Grad-CAM) analysis. The experimental results showcase the outstanding performance of the CGO-ensemble, achieving an impressive accuracy of 100% on MSLD and 94.16% on MSID. Our approach significantly outperforms other state-of-the-art optimization algorithms, traditional ensemble methods, and existing techniques in the context of Mpox detection on these datasets. These findings underscore the effectiveness and superiority of the CGO-Ensemble in accurately identifying Mpox cases, highlighting its potential in disease detection and classification.