Alharbi Amal H, Towfek S K, Abdelhamid Abdelaziz A, Ibrahim Abdelhameed, Eid Marwa M, Khafaga Doaa Sami, Khodadadi Nima, Abualigah Laith, Saber Mohamed
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA.
Biomimetics (Basel). 2023 Jul 16;8(3):313. doi: 10.3390/biomimetics8030313.
The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, -Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, -Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.
导致猴痘的病毒在非洲已被观测到数年，并且它与皮肤损伤的发展有关。新冠疫情之后，病毒感染带来的致命影响引发了公众的恐慌和焦虑。由于新冠疫情已达到大流行级别，快速检测方法至关重要。本研究的总体目标是使用元启发式优化来提高特征选择和分类方法的性能，以便在大流行情况下将皮肤损伤识别为猴痘的指标。使用深度学习和迁移学习方法来提取必要特征。用于特征提取的深度学习框架是GoogLeNet网络。此外，采用二元版本的勺喉优化（DTO）算法进行特征选择。然后使用决策树分类器对所选特征集进行标记。使用DTO算法的连续版本对决策树分类器进行优化，以提高分类准确率。使用各种评估方法，通过以下指标对所提出的方法和其他竞争方法进行比较和对比：准确率、灵敏度、特异性、-值、N-值和F1分数。通过特征选择和决策树分类器，使用所提出的方法取得了以下结果：F1分数为0.92，灵敏度为0.95，特异性为0.61，-值为0.89，N-值为0.79。在对决策树分类器的参数进行优化后，所提出方法的总体准确率为94.35%。此外，已将方差分析（ANOVA）和威尔科克森符号秩检验应用于结果，以研究所提出方法与其他方法之间的统计差异。这种比较验证了所提出的猴痘病例检测方法的独特性和重要性。
