使用深度学习模型对白光和窄带成像内镜图像进行声带白血病的分类
Zhenzhen You1, Botao Han1, Zhenghao Shi1
1Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.
Head & neck
|October 14, 2023
概括
像谷歌LeNet,ResNet和DenseNet这样的深度学习模型可以从图像中准确地分类声带白血病. 这有助于早期诊断和治疗喉癌的决策.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 精确的声带白血症分类对于喉癌的检测和个性化治疗至关重要.
- 为喉任务选择最佳的深度学习模型仍然是一个挑战.
研究的目的:
- 可靠地评估现有的深度学习模型来对声带白血病的分类.
- 为这种特定的医疗应用确定最有效的深度学习技术.
主要方法:
- 创建了白光和窄带成像 (NBI) 声带白血病数据集,具有六种分类.
- 评估了六种经典的深度学习模型:AlexNet,VGG,谷歌初始化,ResNet,DenseNet和Vision Transformer.
主要成果:
- 谷歌LeNet,DenseNet-121和ResNet-152都表现出了很好的分类性能.
- 获得的最高精度为白光图像的0.9583和NBI图像的0.9478.
- 这些模型表现出高灵敏度,特异性和精度.
结论:
- 谷歌LeNet,ResNet和DenseNet提供了精确的声带白血病的病理分类.
- 这有助于早期诊断,并为治疗决策提供信息,降低了内镜医生的负担.
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