组合集和可解释的深度学习框架,用于高精度分类野生食用巨型真菌
Aras Fahrettin Korkmaz1, Fatih Ekinci2, Eda Kumru3
1Faculty of Health Sciences Nutrition, Dietetics Department, Şirinevler Campus, İstanbul Kültür University, 34191 Istanbul, Türkiye.
Biology
|December 30, 2025
概括
准确的野生食用巨型真菌的识别是至关重要的. 结合CNN和可解释AI的新型组合模型实现了97.36%的准确性,改善了食品安全和生物多样性保护.
科学领域:
- 菌类学 菌类学是指菌类学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 准确识别野生食用巨型真菌对于生物多样性,食品安全和生态可持续性至关重要.
- 可食用和有毒物种之间的形态相似性带来了重大识别挑战.
研究的目的:
- 开发和评估先进的机器学习模型,以精确识别野生食用宏观真菌.
- 通过可解释AI (XAI) 技术提高模型的解释性.
主要方法:
- 对24种野生食用宏观真菌物种的精选数据集的分析.
- 基准测试六个最先进的卷积神经网络 (CNN) 和四个组合配置.
- 将EfficientNetB0,ResNet50和RegNetY集成到一个分层投票组合模型中.
- 应用XAI方法 (Grad-CAM,Eigen-CAM,LIME) 来实现模型的可解释性.
主要成果:
- 组合模型实现了最高的精度 (97.36%),AUC (0.9996) 和MCC (0.9725).
- 在个别CNN中,EfficientNetB0表现最好 (95.55%准确率).
- 通过XAI的方法,成功地突出了生物相关的区域,提高了模型的透明度.
结论:
- 工程集体学习与XAI相结合,为细粒度真菌分类提供了强大的和可扩展的解决方案.
- 这种方法大大促进了真菌学研究,并为生态监测和物种识别提供了更广泛的应用潜力.
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