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基于深度学习的微藻类的分类使用光和扫描电子显微镜图像
Mesut Ersin Sonmez1, Betul Altinsoy1, Betul Yilmaz Ozturk2
1Department of Bioengineering, Faculty of Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey.
Micron (Oxford, England : 1993)
|July 5, 2023
概括
深度学习使用光显微镜 (LM) 和扫描电子显微镜 (SEM) 图像准确识别微藻. 这种方法实现了99%的准确性,优于传统方法,并突出了成本效益高的光学显微镜用于藻类识别.
科学领域:
- 海洋生物学 海洋生物学
- 生物技术是生物技术.
- 计算科学 计算科学
背景情况:
- 微藻具有重要的工业和生态作用.
- 藻类繁殖对环境构成重大威胁,需要有效监测.
- 目前的识别方法是耗时的,劳动密集的,昂贵的.
研究的目的:
- 评估使用LM和SEM进行微藻识别的深度学习模型.
- 为了比较各种卷积神经网络 (CNN) 架构的性能.
- 研究将SEM图像与传统LM图像相结合的有效性.
主要方法:
- 使用光显微镜 (LM) 和扫描电子显微镜 (SEM) 拍摄了五种微藻物种的图像 (两种蓝藻,三种叶绿藻).
- 应用了最先进的卷积神经网络 (CNN) 模型:VGG16,MobileNet V2,Xception,NasnetMobile,以及EfficientNetV2.2. 这三种模式都在应用中.
- 首次将SEM图像集成到基于深度学习的微藻识别中.
主要成果:
- 通过VGG16和EfficientNetV2模型,对于LM和SEM图像实现了99%的特殊分类准确度.
- 对于大多数具有SEM图像的模型,已经证明了高分类准确率 (>93%).
- 纳斯网移动显示SEM图像的精度最低 (87%),而其他模型表现良好.
结论:
- 深度学习,特别是CNN,为微藻识别提供了一种快速而精确的方法.
- 将SEM图像与LM图像相结合,可以提高识别的准确性.
- 低成本的光学显微镜与深度学习相结合,与用于藻类识别的电子显微镜相比,取得了更好的结果.
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