一种混合的长期短期记忆 - 卷积神经网络多流深度学习模型,其中包含卷积区注意模块,用于天花检测
Benjamin Appiah Yeboah1, Kojo Sam Micah1, Isaac Acquah1
1Biomedical Engineering Program, Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Science progress
|March 28, 2025
概括
结合LSTM,CNN和CBAM的新深度学习模型有效地检测到mpox. 这种人工智能工具在早期的mopox诊断中显示出高准确度,有助于公共卫生工作.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 麻疹 (Mombox) 是一种致动物性病毒性疾病,引起疼痛的病变,发烧和疲劳.
- 全球爆发,包括非特有地区,需要改进早期检测方法.
- 深度学习有望提高像mpox.com这样的传染病的诊断能力.
研究的目的:
- 开发一种混合深度学习模型,用于早期mopox检测.
- 整合长期短期记忆 (LSTM),卷积神经网络 (CNN) 和卷积区注意模块 (CBAM) 以提高诊断准确度.
主要方法:
- 与CBAM一起开发了一个多流LSTM-CNN模型,并在Mpox皮肤损伤数据集v2.0.0.上进行训练.
- LSTM和CNN层分别用于序列和空间特征提取.
- 使用CBAM进行特征调节,使用LIME和Grad-CAM进行模型解释性.
主要成果:
- 混合型号实现了高性能,精度为94%,F1得分为94%,AUC为95.04%.
- 该模型与现有的最先进的方法相比,显示出具有竞争力的结果.
- 可解释性技术为模型的诊断推理提供了洞察力.
结论:
- 开发的LSTM-CNN-CBAM模型是早期mopox检测的可靠工具.
- 该模型的性能支持其在网络和移动平台上的潜在集成,以实现可访问的诊断.
- 这种人工智能驱动的方法为管理mopox疫情提供了有希望的解决方案.
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