使用卷积神经网络和局部可解释模型-不可知解释技术开发Cynanchum wilfordii和Cynanchum auriculatum的分类模型
Dae-Hyun Jung1, Ho-Youn Kim2,3, Jae Hee Won4
1Department of Smart Farm Science, Kyung Hee University, Yongin-si, Republic of Korea.
Frontiers in plant science
|June 19, 2023
概括
人工智能 (AI) 有助于使用深度学习图像分类来区分药用植物,如Cynanchum wilfordii和类似物种. 这项技术提供了一种可靠的方法来验证草药中植物的身份和质量.
科学领域:
- 植物学 植物学
- 药理学是指药理学,即药理学是指药理学.
- 计算机科学 计算机科学
背景情况:
- Cynanchum wilfordii是一种有价值的药用植物,但由于外观相似,很容易与Cynancum auriculatum混.
- 准确的鉴定对于确保传统草药疗法的有效性和安全性至关重要.
研究的目的:
- 开发和验证基于人工智能的系统,使用图像分类来区分C.wilfordii和C.auriculatum.
- 评估不同深度学习模型对此任务的性能.
- 使用LIME.探索AI模型的可解释性.
主要方法:
- 收集了大约800张C.wilfordii和C.auriculatum截面的图像.
- 利用图像增强来创建一个~3200图像的数据集用于训练.
- 采用深度学习模型,特别是Inception-ResNet和VGGnet-19,用于图像分类.
- 应用局部可解释的模型不可知解释 (LIME) 模型可解释性.
主要成果:
- 与VGGnet-19相比,Inception-ResNet模型显示出更高的性能和更快的学习速度.
- 人工智能分类模型在验证集上取得了大约0.862的强表现.
- LIME成功提供了解释性质,提高了模型的透明度.
结论:
- 通过人工智能增强的深度学习图像分类,可以准确地区分形态上相似的药用植物.
- 人工智能作为一种有价值的辅助工具,用于植物材料的感官评估和质量控制.
- 可解释的人工智能模型对药物鉴定和质量保证的未来应用充满希望.
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