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相关实验视频

Updated: May 15, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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基于深度学习的图像分类和量化模型用于平板电脑粘贴.

Ji Yeon Kim1, Du Hyung Choi1

  • 1College of Pharmacy, Daegu Catholic University, Gyeongsan-si, Gyeongbuk 38430, Republic of Korea.

International journal of pharmaceutics
|May 8, 2025
PubMed
概括

一个使用卷积神经网络 (CNN) 和灰色级共发生矩阵 (GLCM) 的新型综合模型功能有效地分类和量化药品制造中的药片粘合. 这种先进的系统可确保药物产品的质量,并提高制造效率.

科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 药物产品质量控制 药物产品质量控制
  • 计算成像技术的成像

背景情况:

  • 药片粘贴是影响药品质量,制造效率和治疗功效的关键问题.
  • 像视觉检查和质量属性测试这样的传统方法可能无法检测到微妙的粘合问题.
  • 开发自动化,敏感的检测方法,用于平板电脑粘贴,对于强大的制药生产至关重要.

研究的目的:

  • 开发和验证一种新型的综合模型,用于药品制造中分类和量化粘贴片.
  • 为了评估不同卷积神经网络 (CNN) 架构的性能,用于平板电脑粘贴分类.
  • 评估综合模型在检测可能被传统质量控制措施遗漏的轻度粘附方面的能力.

主要方法:

  • 开发了一种集成模型,将卷积神经网络 (CNN) 架构 (AlexNet,VGG 16,ResNet 50,GoogLeNet) 与灰级共发生矩阵 (GLCM) 特性和支向量机器相结合.
  • 谷歌LeNet在平板电脑粘贴的分类方面表现出了卓越的表现.
  • 分析了GLCM特征,以量化粘合严重程度,并在旋转平板压力机上验证了一种集成的分类量化模型.

主要成果:

  • 谷歌LeNet获得了最高的分类准确度 (99.39%),精度 (100.00%),回忆 (98.78%) 和F1得分 (99.38%).
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.图像分析 图像分析坚持坚持坚持的坚持.支持矢量机器的支持矢量机器.视觉检查 视觉检查 视觉检查

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  • 量化模型成功地使用粘合指数识别和测量了粘合区域,显示了粘合和非粘合区域之间的显著差异.
  • 验证证明了该模型能够检测到最小和分类的粘度水平,并且具有很高的重复性,即使平板电脑的质量属性保持在可接受的范围内.

    • 结论:

    • 拟议的CNN-GLCM-SVM集成模型有效地分类和量化平板电脑粘贴,超越了视觉检查和标准质量属性测试的局限性.
    • 这种先进的检测系统可以显著提高制药制造效率,并通过识别轻微的粘合问题来确保药品质量的一致性.
    • 该模型提供了一个强大的解决方案,用于加强药片制造中的质量控制,确保患者安全和药物的有效性.