使用YOLOv5检测肠道寄生虫卵的高效和有效的框架
Satish Kumar1, Tasleem Arif1, Gulfam Ahamad2
1Department of Information Technology, BGSB University, Rajouri 185131, India.
Diagnostics (Basel, Switzerland)
|September 28, 2023
概括
深度学习计算机视觉精确地从图像中检测肠道寄生虫卵,每样只需8.5毫秒就能达到97%的精度. 这加快了诊断,并减少了对寄生虫感染的专家负担.
科学领域:
- 医疗成像医学成像
- 寄生虫学的寄生虫学
- 计算机视觉 计算机视觉
背景情况:
- 肠道寄生虫感染是全球主要的健康问题,特别是在热带地区.
- 目前用于诊断的手动显微镜是缓慢的,昂贵的,需要专门的专业知识.
- 深度学习,特别是卷积神经网络,对图像分析具有前景,但在寄生虫学中未得到充分利用.
研究的目的:
- 开发和评估一种新的深度学习模型,用于从图像中检测和分类肠道寄生虫卵.
- 为了提高肠道寄生虫诊断的速度和准确性.
- 为了减少医疗专家的工作量,并促进及时的患者治疗.
主要方法:
- 转移学习架构用于图像分析.
- 使用了图像预处理和增强技术.
- 实施了YOLOv5算法来检测和分类寄生虫卵.
主要成果:
- 拟议的模型实现了大约97%的平均平均精度.
- 每个样品的检测时间非常快,平均只有8.5毫秒.
- 该系统在5393张肠道寄生虫图像的数据集上进行了训练和验证.
结论:
- 开发的深度学习方法为检测肠道寄生虫卵提供了高效和准确的方法.
- 这项技术有可能成为临床环境中实时诊断工具的基础.
- 这些发现提升了寄生虫感染的医学成像和诊断能力.
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