基于卷积神经网络的声带瘤分类技术用于家庭自行预查目的
Gun Ho Kim1,2, Young Jun Hwang3, Hongje Lee4
1Medical Research Institute, Pusan National University, Yangsan, Korea.
Biomedical engineering online
|August 18, 2023
概括
一种新的深度学习技术可以通过家庭自我查来早期检测良性声带瘤. 这种方法可以准确地识别和分类各种瘤类型,有助于及时诊断和改善患者的治疗结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 声带瘤需要早期检测以获得更好的预后.
- 目前的查方法可能不易用于家庭自我评估.
- 良性声带瘤包括囊,颗粒瘤,白血病,结节和息肉.
研究的目的:
- 开发一种深度学习技术,同时检测和分类良性声带瘤.
- 为了实现早期瘤检测的简化家庭自预查.
- 为了改善声带瘤在良性阶段的早期诊断.
主要方法:
- 实施四个卷积神经网络 (CNN) 模型:两个Mask R-CNN,Yolo V4和一个单射探测器.
- 培训,验证和测试使用2183个喉腔镜图像的模型.
- 基于F1分数和不同瘤类型的假阴性率的模型性能评估.
主要成果:
- 在所有测试的良性声带瘤类型中,Yolo V4表现出最高的F1分数.
- 具体的模型显示了不同瘤分类的最低假阴性率 (Yolo V4用于囊/粒瘤,Mask R-CNN用于其他类型).
- 一个嵌入式Yolo V4模型实现了与其计算机操作对应器可比的性能.
结论:
- 基于深度学习的家庭查技术显示了早期声带瘤检测的潜力.
- 提出的方法可以帮助改善声带瘤患者的长期存活率.
- 由人工智能驱动的工具可以促进可访问和有效的声带健康监测.
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