计算机视觉模型用于检测犬类皮下炎和爪瘤
Andrew Smith1, Patrick W Carroll1, Srikanth Aravamuthan1
1School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA.
Veterinary dermatology
|December 7, 2023
概括
一个新的AI工具Pawgnosis使用计算机视觉快速检测狗爪病如皮下炎和瘤. 这种兽医皮肤病学工具在初步测试中取得了很高的准确性,显示了对在狗中早期发现疾病的希望.
科学领域:
- 兽医皮肤病学 兽医皮肤病学
- 人工智能 (AI) 是一种人工智能.
- 计算机视觉 (CV) 是指计算机视觉.
背景情况:
- 人工智能,特别是卷积神经网络 (CNN),通过图像分析进行早期诊断,使人类皮肤病学进步.
- 计算机视觉 (CV) 显示出识别狗皮肤疾病的巨大潜力,这是兽医实践中常见的问题.
- 目前,没有专门的CV工具可用于检测狗爪病,如皮下炎或瘤.
研究的目的:
- 评估Pawgnosis,一种新的物体检测模型,利用Tiny YOLOv4快速识别狗爪病.
- 评估该模型在检测狗爪下皮炎和瘤方面的能力.
主要方法:
- 一个Tiny YOLOv4图像分析模型 (Pawgnosis) 被开发并部署在带有摄像头的微型计算机上.
- 来自健康狗和患有皮下炎或瘤的狗的爪子的数字图像被用于测试.
- 使用包括平均平均精度 (mAP),精度,回忆和每秒 (FPS) 在内的指标来评估模型性能.
主要成果:
- 在一个大型单标签数据集 (数据集A) 上训练的Tiny YOLOv4模型获得了最佳性能.
- 该模型的平均mAP值为0.95,精度为0.86,回忆率为0.93.
- 该系统以每秒20 (FPS) 的速度运行,表明了快速检测能力.
结论:
- 帕格诺斯对象检测模型显示了在兽医皮肤病学中实际应用的巨大潜力.
- 这种人工智能驱动的工具可以促进早期检测和监测狗爪病.
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