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使用显微镜的高分辨率CMOS传感器捕获的图像上的Ixodes ricinus的深度学习方法

Aleksandra Marzec^1,2, Anna Filipowska², Oliwia Humeniuk²

¹Foundation of Cardiac Surgery Development, Institute of Heart Prostheses, 345a Wolności, 41-800 Zabrze, Poland.

Sensors (Basel, Switzerland)

|August 28, 2025

概括

这项研究引入了一个深度学习模型来分类欧洲的发育阶段. 这种人工智能工具有助于通过准确识别幼虫,和成年人来了解传播的疾病.

科学领域:

昆虫学
计算机科学
公共卫生

背景情况:

它是欧洲最常见的虫,也是莱姆病菌和TBEV等病原体的重要载体.
不同的发育阶段 (幼虫,,成年雌性,成年雄性) 在疾病传播中起着不同的作用.
由于它们的发病率和小体积,在流行病学上尤为重要,因此精确的鉴定至关重要.

研究的目的:

开发和验证深度学习模型来分类的发育阶段.
解决自动识别欧洲物种阶段的需求,与现有的以北美为重点的解决方案区别.

主要方法:

一个卷积神经网络 (CNN) 模型被开发和训练在微观图像的.
图像数据来自波兰上西里西亚地区.
使用可解释人工智能 (XAI) 技术Grad-CAM可视化模型决策过程.

主要成果:

通过CNN模型对的发育阶段进行了有效分类.
这项研究是首次将CNN用于识别欧洲动物的发育阶段.
格拉德-CAM分析提供了对模型分类精度至关重要的图像特征的见解.

结论:

开发的深度学习方法提供了一种新且准确的方法来识别的发育阶段.
这项技术在昆虫学研究,公共卫生监测和传播疾病管理方面具有重要的潜在应用.
该模型为区分病阶段提供了有价值的工具,改善了流行病学评估和控制策略.

关键词:

其他国家可解释的AI (XAI)一个Grad-CAM 鱼类深度学习图像分类的发展阶段

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