通过YOLOv7模型对口腔粘膜病变进行分类
Yu Hsu1, Cheng-Ying Chou2, Yu-Cheng Huang3
1Department of Medical Imaging, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
概括
像YOLOv7这样的深度学习模型对早期口腔癌检测有希望. 这些计算机视觉工具准确地分类口腔病变,有助于及时诊断和治疗.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 口腔瘤学 在口腔瘤学
背景情况:
- 全球唇部和口腔癌的发病率不断上升.
- 需要先进的早期检测方法.
研究的目的:
- 用计算机视觉和深度学习来提高口腔粘膜病变的早期检测和分类.
- 评估YOLOv7模型对口腔病变分类的性能.
主要方法:
- 在口腔粘膜病变的大型数据集 (>50,000张图像) 上训练了YOLOv7深度学习模型.
- 分类病变为良性,潜在恶性和恶性等级.
- 对各种YOLOv7配置的性能进行比较,包括YOLOv7-D6-CA.
主要成果:
- 对于所有类型的损伤,YOLOv7模型表现出高精度和回忆能力.
- YOLOv7-D6模型在识别恶性病变方面表现出色.
- 协调注意力集成 (YOLOv7-D6-CA) 提高了分类准确性.
结论:
- YOLOv7模型对于分类和分类口腔病变是有效的.
- 深度学习为临床和远程环境中早期检测口腔癌提供了宝贵的工具.
相关概念视频
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