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一个适用于农村地区的轻量级智能喉癌检测系统

Yi-Fan Kang¹, Lie Yang², Kai Xu¹

¹Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.

American journal of otolaryngology

|August 13, 2024

概括

一个用于喉癌 (LC) 检测的智能系统有助于在农村地区早期诊断. 与MobileNet集成的深度学习模型显示出高准确性和效率,改善了专家稀缺的地方的选.

科学领域:

人工智能在医学中的应用
医学成像分析医学成像分析
瘤学诊断诊断瘤学

背景情况:

喉癌 (LC) 的早期诊断至关重要,特别是在农村环境中,专家的接触有限.
现有的深度学习模型用于LC识别在资源有限的环境中面临挑战.
开发高效准确的诊断工具对于改善服务不足地区的医疗保健至关重要.

研究的目的:

开发一个智能喉癌检测系统 (ILCDS),利用深度学习在农村地区进行有效的喉癌查.
评估各种深度学习模型的性能和效率,用于LC识别.
根据资源限制,选择最适合融入ILCDS的模型.

主要方法:

一个2023年喉镜图像的数据集被编译和增强.
八个深度学习模型 (AlexNet,VGG,ResNet,DenseNet,MobileNet,ShuffleNet,Vision Transformer,Swin Transformer) 被应用于LC的识别.
模型的评估是基于性能 (准确性) 和效率 (推断速度,大小) 的.

主要成果:

所有评估的模型在测试组中都实现了超过90%的准确性.
VGG,DenseNet和MobileNet表现出卓越的性能,准确度超过95%.这是一个非常好的例子.
由于其紧的尺寸和快速推断,MobileNet被确定为最有效的模型,使其成为ILCDS的理想选择.

结论:

在资源有限的农村地区,ILCDS显示了提高LC查准确性的巨大潜力.
该系统的适度计算要求使其适合在计算机资源有限的环境中部署.
ILCDS可以帮助减轻耳鼻喉科医生的工作量,改善诊断的可访问性.

关键词:

深度学习模型深度学习模型喉癌是一种喉癌.喉腔镜检查 (Laryngoscopy) 是一种耳鼻镜检查.移动网络 (MobileNet) 是一个移动网络.农村地区农村地区

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