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深度学习用于使用光学图像进行中耳炎分类

Qingqing Guo¹, Liangzhen Xie², Ling Zhou²

¹Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.

|December 2, 2025

概括

深度学习模型准确地对中耳炎 (OM) 进行分类. VGGNet-19获得了94.51%的准确性,显示了这种常见耳部感染的自动诊断潜力.

科学领域:

医疗成像医学成像
人工智能的人工智能
耳鼻喉科耳鼻喉科耳鼻喉科

背景情况:

中耳炎 (OM) 是一种广泛的健康问题,影响听力和一般健康.
目前的OM诊断方法依赖于主观的耳视镜图像解释,导致诊断不一致和错误.

研究的目的:

评估五种深度学习模型在对中耳炎诊断中耳镜图像进行分类时的有效性.
确定最有效的深度学习模型来区分正常耳朵和各种类型的中耳炎.

主要方法:

采用了819张耳视镜图像的数据集,分为正常,急性中耳炎,带出血的中耳炎和慢性性中耳炎.
在5个深度学习模型 (ResNet-18,GoogleLeNet,AlexNet,MobileNet-V3,VGGNet-19) 上,分别对60%和20%的数据进行了训练和验证.
模型性能被严格评估,使用准确度,灵敏度,特异性,精度,F1得分和ROC-AUC分析对20%的测试组进行了严格评估.

主要成果:

VGGNet-19表现出卓越的性能,实现了最高的精度 (94.51%),灵敏度 (94.18%),特异性 (98.10%),精度 (93.86%).
VGGNet-19模型在识别慢性性中耳炎方面表现出了卓越的能力.
虽然其他评估的模型提供了具有竞争力的结果,但VGGNet-19在关键绩效指标上始终表现优于它们.

结论:

VGGNet-19显示出作为中耳炎分类的准确和自动化工具的巨大潜力.
这些发现支持深度学习的整合,以提高中耳炎的诊断准确性.
建议进行进一步的研究,以解决数据集失衡问题,并在各种临床环境中验证这些发现,以提高概括性.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.模型评价模型评价中耳炎的诊断中耳炎的诊断奥托斯科普图像分类的分类方法奥托斯科普图像数据集数据集

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