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卷积神经网络的实施用于微生物殖民地识别

Fanhui Kong^1,2, Mingkuan Su^1,2, Jianfeng Guo^1,2

¹Department of Laboratory Medicine, Mindong Hospital of Ningde City, Ningde, Fujian, China.

Microbiology spectrum

|July 23, 2025

概括

深度学习模型,特别是卷积神经网络 (CNN),准确地分类微生物群落,帮助临床识别. 谷歌LeNet实现了98.80%的准确性,为微生物学家提供了一个客观的工具.

科学领域:

微生物学微生物学
人工智能的人工智能
计算机视觉计算机视觉

背景情况:

微生物分类在很大程度上依赖于熟练的微生物学家主观的视觉识别.
这种主观性可能会导致临床分类中的不一致性,原因是不同的专业知识和解释.

研究的目的:

应用深度学习来实现微生物殖民地自动识别和分类.
开发一个客观的,数据驱动的工具,以协助微生物学家在临床识别任务.

主要方法:

用临床隔离的微生物殖民地照片创建了一个48x48像素的数据集.
八个卷积神经网络 (CNN) 被训练和评估用于殖民地分类.
绩效指标包括准确性,精度,回忆力和F1分数,这些分类包括格兰氏阴性细菌,格兰氏阳性菌,Candida和Aspergillus.

主要成果:

谷歌LeNet显示了最高的分类准确率为98.80%,其他模型如MobileNet和ShuffleNet也超过了98%的准确率.
这些模型显示了高的概括性能,正确识别了培训数据中不存在的菌株.
在500张测试图像中,GoogleLeNet只错误分类了6张.

结论:

深度学习,特别是CNN,为微生物殖民地识别提供了强大而准确的方法.
这些人工智能驱动的工具可以作为微生物学家的有价值的辅助决策辅助工具,提高客观性和效率.
开发的方法是实用的,不需要专门的设备,并且在各种微生物菌株上表现出强的性能.

关键词:

算法算法是一种算法.殖民地殖民地是一个殖民地.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.微生物是微生物.

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