一个基于YOLOv8的轻量级模型用于胃癌检测
Seung-Won Jeong1, Shabir Ahmad2, Jae-Seoung Kim3
1Department of Computer Engineering, Gachon University, Gyeonggi-do 13120, Republic of Korea.
Computers in biology and medicine
|August 5, 2025
概括
这项研究引入了一种优化的YOLOv8模型,用于实时检测胃癌,在各种计算机处理器中实现高精度. 该模型表现出强大的性能,使深度学习在各种医疗环境中更容易获得.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 深度学习模型对胃癌检测有希望,但它们的性能依赖于处理器.
- 现实世界中的医疗环境使用不同性能级别的多种计算机处理器 (CPU和GPU).
研究的目的:
- 使用YOLOv8开发一种胃癌检测模型,实现实时性能,对处理器变化不那么敏感.
- 评估模型在不同硬件的实际医疗领域的可行性.
主要方法:
- 提出了一种经过修改的YOLOv8模型,在脊柱中结合了Ghost卷积和SE块,以在子和头部获得通道智能的注意力.
- 评估了模型的检测精度和推断速度在一个CPU和四个GPU上,性能各不相同.
- 将拟议的模型与基线YOLOv8-n和YOLOv8-m进行比较.
主要成果:
- 拟议的模型实现了77.5%的平均精度 (mAP),超过YOLOv8-m (76.5% mAP) 和YOLOv8-n (74.4% mAP) 的4.16%.
- 在不同的GPU性能中保持实时推断速度.
- 该模型具有最小的复杂性,参数为2.8M,GFLOP为7.7.
结论:
- 开发的基于YOLOv8的模型提供了高检测精度和实时性能,可适应各种计算机处理器.
- 这项研究支持深度学习在各种临床环境中用于胃癌检测的实际应用.
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