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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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使用深度神经网络检测水.

Amir Sorayaie Azar1, Amin Naemi2, Samin Babaei Rikan1

  • 1Department of Computer Engineering, Urmia University, Urmia, Iran.

BMC infectious diseases
|June 27, 2023
PubMed
概括
此摘要是机器生成的。

一个DenseNet201深度神经网络模型准确地从皮肤图像中检测到水,优于其他模型. 像LIME和Grad-Cam这样的可解释AI技术通过突出显示受影响的皮肤区域来增强诊断信心.

关键词:
人工智能的人工智能深度学习是一种深度学习.这是一场流行性流行病.可解释的人工智能这是一个 Grad-cam.在 LIME 时代,的水是的水.

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科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 皮肤病学 皮肤病学

背景情况:

  • 2022年水疫情凸显了快速诊断工具的需要.
  • 从COVID-19大流行中吸取的经验教训强调了疫情准备的重要性.
  • 全球健康问题需要先进的疾病检测方法.

研究的目的:

  • 开发和评估深度神经网络 (DNN) 模型,用于使用皮肤图像检测水.
  • 为了比较七个不同的DNN架构在识别Monkeypox的性能.
  • 通过可解释的人工智能增强诊断透明度和临床信任.

主要方法:

  • 使用了一组皮肤图像数据集,包括,水,麻疹和正常病例.
  • 七个DNN模型被开发和测试在两个类 (Monkeypox和其他人) 和四类 (所有类别) 场景.
  • 对于模型的可解释性,使用了局部可解释模型不可知解释 (LIME) 和梯度加权类激活映射 (Grad-Cam).

主要成果:

  • 在两个类的场景中,DenseNet201模型实现了最高的性能,准确率为97.63%,F1-Score为90.51%.
  • 在四个类的场景中,DenseNet201达到95.18%的准确率和89.61%的F1-Score.
  • LIME和Grad-Cam成功地确定了对Monkeypox诊断有贡献的关键图像区域,从而提高了模型的解释性.

结论:

  • DenseNet201模型在从皮肤图像中检测水方面表现出卓越的性能.
  • 可解释的AI技术 (LIME,Grad-Cam) 对于理解诊断决策和建立临床信任至关重要.
  • 拟议的模型可以作为一种有价值的辅助工具来诊断水,并将其与类似的皮肤疾病区分开来.