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Mpox-XDE:一个集体模型,利用深度CNN和可解释的AI来检测和分类麻疹.

Dip Kumar Saha1, Sadman Rafi2, M F Mridha3

  • 1Department of CSE, Stamford University Bangladesh, Siddeswari, Dhaka, Bangladesh.

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概括
此摘要是机器生成的。

早期检测 (Mpox) 是至关重要的. 一个新的集体深度学习模型,Mpox-XDE,从皮肤图像中准确识别Mpox,达到98.70%的准确性.

关键词:
深度学习是一种深度学习.检测 检测 检测 检测 检测组合模型模型组合模型的水是的水.蒙波克斯 (Mox Mpox) 是一个在XAI,XAI就是XAI.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 皮肤病学 皮肤病学

背景情况:

  • 人类水 (Mpox) 是一个日益严重的全球健康问题,需要及早识别.
  • 目前用于Mpox检测的深度学习 (DL) 模型需要进一步提高早期诊断的可靠性.
  • 准确和及时的诊断对于防止Mopox的传播至关重要.

研究的目的:

  • 开发一个强大而准确的集体深度学习模型,用于早期Mpox检测.
  • 为了提高现有的DL模型的分类性能,用于Mpox识别.
  • 为医疗保健专业人员提供可靠的工具,用于诊断Mpox.

主要方法:

  • 通过将三个修改后的DL模型:Xception,DenseNet201和EfficientNetB7.7结合起来,创建了一个集合模型Mpox-XDE.
  • 为了培训和测试,使用了770张图像的Mpox皮肤图像数据集 (MSID).
  • 整体模型包含Softmax,密度和平的层与掉落,以及用于分类的全球平均聚合层.

主要成果:

  • 莫波克斯-XDE模型实现了高性能指标:98.70%的测试准确度,98.90%的精度,98.80%的回忆率和98.80%的F1分数.
  • 该模型成功地将图像分为四类:水,麻疹,正常和Mopox.
  • 使用Grad-CAM的可解释AI (XAI) 可视化模型的决策过程,突出显示相关的图像区域.

结论:

  • 拟议的Mpox-XDE组合模型在从皮肤图像中早期检测Mpox方面表现出极高的准确性.
  • 这种方法为Mpox.的诊断工具提供了显著的进步.
  • 可解释性功能有助于理解和信任模型对临床应用的预测.