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开发人工智能聊天机器人用于蛇形象分类和准确性改进

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  • 1Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.

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此摘要是机器生成的。

使用Swin Transformer v2的机器学习模型可以从图像中准确识别蛇种. 一种测试时间物体检测和裁剪 (TT-ODC) 方法显著提高了蛇的真实准确性.

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科学领域:

  • * 人工智能
  • * 杂虫学
  • * 医疗信息学

背景情况:

  • * 蛇咬对全球健康造成重大负担,需要快速准确地识别蛇物种以进行有效的抗毒选择.
  • * 准确的物种识别对于在蛇咬后进行适当的临床治疗至关重要.

研究的目的:

  • * 评估深度学习模型的有效性,特别是Swin Transformer v2,用于使用现实图像数据对台湾的蛇类进行分类.
  • * 评估一种新的预处理技术,即测试时间对象检测和裁剪 (TT-ODC) 对具有挑战性的外部图像数据集的模型性能的影响.

主要方法:

  • 基于Swin Transformer v2架构的深度学习模型的开发,利用标记的蛇形图像的大数据集进行转移学习.
  • * 培训和验证使用来自30573图像数据集的12,000图像. 通过LINE聊天机器人和社交媒体收集的2400张图像进行了评估.
  • 在外部测试组上实施和评估TT-ODC预处理方法,以提高图像质量和模型准确性.

主要成果:

  • 在内部验证组中,Swin变压器v2模型获得了95.6%的准确性.
  • 在没有预处理的情况下,外部测试组的性能为83. 3%.
  • * TT-ODC预处理的应用提高了外部测试集的准确性,达到89.8%,接近人类专家的性能 (90.3%).

结论:

  • *将Swin Transformer v2模型与TT-ODC预处理方法集成,为临床环境中的蛇物种识别提供了实用和准确的解决方案.
  • 这种方法显著提高了人工智能驱动的蛇识别工具的可靠性,特别是在处理来自各种真实世界的图像时.
  • 开发的系统为改善蛇治疗和患者的治疗结果提供了有价值的工具.