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使用深度学习检测背部舌头表面的异常变化

Ho-Jun Song¹, Yeong-Joon Park¹, Hie-Yong Jeong²

¹Department of Dental Materials, Dental Science Research Institute, School of Dentistry, Chonnam National University, Gwangju 61186, Republic of Korea.

Medicina (Kaunas, Lithuania)

|July 29, 2023

概括

深度学习从图像中准确地检测出异常的舌头区域,有助于诊断粘膜疾病. 这种人工智能工具帮助临床医生识别舌头异常.

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
口腔病理学诊断口腔病理学诊断

背景情况:

舌头粘膜的变化表明了各种局部和全身健康状况.
早期发现背舌异常对于及时诊断和治疗至关重要.
当前的诊断方法可能会从先进的计算方法中受益.

研究的目的:

评估深度学习 (DL) 在检测舌头背面异常区域的有效性.
评估VGG16模型在分类正常和异常舌头图像方面的表现.
调查点映射细分的实用性,用于划分异常的舌头区域.

主要方法:

收集了175张背部舌头图像的数据集,并将其处理成7782张切割图像.
训练VGG16深度学习模型将图像区域分类为正常,异常或非语言.
对80张全视图背部舌头图像进行了点映射细分,用于异常区域分析.

主要成果:

在图像预测中,VGG16模型在异常 (0.960) 和正常 (0.968) 类中获得了高F1分.
点映射细分产生了异常区域的平均F1分数为0.727,正常区域的平均F1分数为0.645.
细分评估显示,异常区域的平均精度为0.940,正常区域的平均精度为0.890.

结论:

开发的深度学习算法在识别异常的背部舌头表面区域方面表现出高准确性.
这种由人工智能驱动的方法显示出作为一种有价值的工具来协助诊断舌头粘膜疾病的潜力.
这些发现支持将深度学习纳入临床实践,以加强口腔健康诊断.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.背部的舌头背部的舌头在口腔的粘膜.舌头舌头舌头舌头舌头疾病舌头疾病在舌头的粘膜.

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