可解释的深度学习框架可用于可靠的物种级分类在Genera Desmodesmus和Tetradesmus内
İlknur Meriç Turgut1, Dilara Gerdan Koc2, Özden Fakıoğlu3
1Department of Fisheries and Aquaculture Engineering, Faculty of Agriculture, Ankara University, 06110 Ankara, Türkiye.
Biology
|January 10, 2026
概括
深度学习和可解释AI (XAI) 准确地使用图像分析对绿色微藻类 (Chlorophyta) 进行分类. 这种方法可靠地识别物种,有助于生物多样性和生物燃料应用.
科学领域:
- * 藻类学和计算生物学:专注于使用先进的计算技术对微藻类的分类.
- *真核生物光合作用:研究光合作用真核生物,特别是绿色微藻.
背景情况:
- * 微藻具有显著的形态多样性,环境可塑性使其复杂化,使分类学解决具有挑战性.
- *准确的分类对于生态监测,生物质优化和生物燃料生产至关重要.
研究的目的:
- *通过合并深度学习和可解释的人工智能 (XAI) 来开发绿色微藻 (Chlorophyta) 分类的透明,可靠的框架.
- * 评估各种深度学习模型的性能和生物相关性,用于识别微藻类物种.
主要方法:
- *使用12个卷积神经网络 (例如,EfficientNet,ResNet152V2) 来分析来自三种叶绿植物的3624个显微镜图像.
- *标准化预处理和数据增强,包括对比度增强和规范化.
- *使用精度和F1评分进行性能评估;通过突出性图和Grad-CAM进行解释性评估.
主要成果:
- *ResNet152V2获得了最高的性能,在宏观F1得分中超过了其他架构.
- *XAI可视化 (盐度图,Grad-CAM) 证实了专注于生物相关特征 (细胞壁,装饰) 的模型.
- *即使使用受限制的数据集,也实现了高测试准确性,证明了模型可靠性.
结论:
- * 深度学习和XAI的整合为自动化微藻分类提供了一个强大的方法.
- *这种方法支持生物多样性监测,生态评估以及生物质和生物柴油生产的优化.
- * 该研究强调了可解释AI在生物分类任务中的实用性.
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