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CGO-ensemble:基于混沌游戏优化算法的深度神经网络的融合,用于准确的Mpox检测
Sohaib Asif1, Ming Zhao1, Yangfan Li1
1School of Computer Science and Engineering, Central South University, Changsha, China.
概括
一个新的CGO-Ensemble框架使用转移学习和混乱游戏优化准确检测Mpox感染. 与现有技术相比,这种方法显著提高了Mpox皮肤病变的诊断准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 全球Mopox发病率不断上升,需要改进诊断工具.
- 传统的组合方法在Mpox检测准确度方面存在局限性.
- 准确识别Mpox对于有效的疾病控制至关重要.
研究的目的:
- 引入一个创新的CGO-Ensemble框架,用于增强Mpox感染检测.
- 提高Mopox诊断的准确性,使用一种新的基于元启发的整体方法.
- 评估拟议框架在基准Mpox数据集上的表现.
主要方法:
- 利用了五个转移学习基础模型,其中包括特征集成和残余块,用于皮肤图像分析.
- 采用加权平均化方案,将基准模型的预测结合起来.
- 利用混沌游戏优化 (CGO) 算法在合奏中实现最佳的重量分配.
- 在Mopox皮肤损伤数据集 (MSLD) 和Mopox皮肤图像数据集 (MSID) 上进行了实验.
- 执行梯度类激活映射 (Grad-CAM) 以实现模型可解释性.
主要成果:
- 在MSLD上达到100%的准确性,在MSID上达到94.16%的准确性.
- 与单个模型,传统集合方法和其他优化算法相比,表现出优异的性能.
- 在Mpox检测中,CGO-Ensemble显著提高了预测准确度.
- 根据Grad-CAM分析,可以了解基准模型的决策过程.
结论:
- 对于准确的Mpox病例识别,CGO-Ensemble框架非常有效和优越.
- 这种方法显示出改善临床环境中疾病检测和分类的巨大潜力.
- 这项研究强调了基于元启发的组合方法在传染病医疗图像分析中的有效性.
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