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从使用深度神经网络的便显微镜图像对Giardia感染进行自动分类

Pezhman Yarahmadi¹, Ehsan Ahmadpour², Parham Moradi³

¹Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences Tabriz, Iran.

BioImpacts : BI

|March 31, 2025

概括

深度学习模型在便图像中准确地分类Giardia lamblia寄生虫. EfficientNet-B0实现了高性能,帮助快速诊断巨病并改善了患者的护理.

科学领域:

医学寄生虫学医学寄生虫学
计算生物学计算生物学
图像分析图像分析

背景情况:

由Giardia lamblia引起的巨病需要及时诊断才能有效治疗.
从便图像中自动分类Giardia对于诊断准确性至关重要.
现有的研究往往侧重于水样,需要研究基于便的图像分析.

研究的目的:

开发和评估深度学习模型,用于在便显微镜图像中自动分类Giardia lamblia.
为了比较Xception,ResNet-50和EfficientNet-B0模型在此分类任务中的性能.
评估自动图像分析在改善病诊断方面的潜力.

主要方法:

一个数据集由1610个智能手机捕获的便显微镜图像进行了策划.
图像使用对比限度自适应基因图平衡 (CLAHE) 进行预处理.
三种深度学习模型 (Xception,ResNet-50,EfficientNet-B0) 使用转移学习进行训练和微调.

主要成果:

在分类Giardia lamblia方面,EfficientNet-B0的表现优于Xception和ResNet-50.
EfficientNet-B0实现了高性能指标:精度 (0.9599),准确度 (0.9629),回忆 (0.9619),特异性 (0.9821),以及F1得分 (0.9607).
该模型成功地区分了正常,囊和形状.

结论:

EfficientNet-B0显示出在便样本中精确自动检测Giardia lamblia的巨大潜力.
这种自动化方法可以帮助实验室专家和寄生虫学家快速诊断病.
这些发现表明,通过增强诊断能力,改善了患者护理和治疗结果.

关键词:

分类分类分类分类.深度神经网络是一个神经网络.这是一种巨病 (giardiasis).机器学习是机器学习.

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