一种基于深度学习和可解释的人工智能的方法来对Discomycetes物种进行分类
Aras Fahrettin Korkmaz1, Fatih Ekinci2, Şehmus Altaş3
1Faculty of Health Sciences Nutrition, Dietetics Department, Şirinevler Campus, İstanbul Kültür University, 34191 Istanbul, Türkiye.
Biology
|June 26, 2025
概括
像EfficientNet-B0这样的深度学习模型准确地对Discomycetes物种进行了分类. 可解释的人工智能 (XAI) 确保可靠的结果,促进真菌识别和生物多样性保护.
科学领域:
- 菌类学 菌类学是指菌类学.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 传统的真菌分类学在精确的物种差异化方面面临着挑战.
- 深度学习的进步为生物分类提供了新的可能性.
- 可解释的人工智能 (XAI) 对于理解和信任科学研究中的AI模型至关重要.
研究的目的:
- 开发和评估深度学习模型来分类Discomycetes物种.
- 通过使用XAI技术来提高模型的可解释性和透明度.
- 为生态应用提高真菌物种识别的准确性和效率.
主要方法:
- 实施包括EfficientNet-B0,MobileNetV3-L,ShuffleNet和EfficientNet-B4.4在内的深度学习模型.
- 使用XAI技术,如Grad-CAM和Score-CAM用于模型可解释性.
- 基于准确性,F1得分和AUC指标的性能评估.
主要成果:
- EfficientNet-B0实现了最高的性能,准确率为97%,F1得分为97%,AUC为99%.
- 移动网络V3-L和ShuffleNet也表现出强大的分类能力.
- XAI技术为模型决策提供了洞察力,确保了可靠的分类.
结论:
- 深度学习模型,特别是EfficientNet-B0,对于Discomycetes物种分类非常有效.
- XAI集成提高了AI在生物科学中的可信度和透明度.
- 这种方法支持生物多样性保护,并为人工智能驱动的生物研究制定了新的标准.
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