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从图像到诊断:舌头病变中的卷积神经网络

Merve Hacer Talu¹, Sümeyye Coşgun-Baybars², Çağla Danacı³

¹Department of Oral and Maxillofacial Radiology, School of Dentistry, Fırat University, Elazığ, Türkiye. dtmerveduran@gmail.com.

Journal of imaging informatics in medicine

|May 5, 2025

概括

人工智能,使用卷积神经网络,准确地分类常见的舌头病变. 这种人工智能工具提高了牙科检查的诊断精度,提供了一种可靠的,非侵入性的方法来识别各种舌头疾病.

科学领域:

口腔医学是指口腔医学.
医疗保健中的人工智能
医学成像分析医学成像分析

背景情况:

临床舌头检查对于诊断全身和局部疾病至关重要.
传统的舌头病变诊断方法往往是主观的.
人工智能 (AI),特别是卷积神经网络 (CNN),显示了改善医学图像分析和诊断准确性的潜力.

研究的目的:

用CNNs对常见的舌头病变进行分类.
通过人工智能提高日常牙科检查的诊断精度.
评估ResNet18和ResNet50模型在舌头病变分类中的性能.

主要方法:

分析了1038张舌头图像的数据集,分为六个类别:健康,涂层,裂纹,毛发,地理和中间方形光.
ResNet18模型用于二进制分类,ResNet50用于三类分类.
图像预处理包括大小调整和增强;性能指标是准确性,精度,回忆和F1分数.

主要成果:

ResNet18在区分健康与毛的舌头病变方面实现了100%的准确性.
ResNet50在健康毛发的分类中达到96%的准确性,但在其他损伤群体中存在挑战.
基于CNN的模型被证明是对分类舌头病变的有效和非侵入性,ResNet18在二进制任务中表现出色.

结论:

人工智能驱动的CNN模型为分类舌头病变提供了有效的工具,提高了诊断可靠性.
人工智能在面放射学中的整合可以增强日常牙科诊断.
建议对更大的数据集和实时临床应用进行进一步的研究,以改进AI诊断工具.

关键词:

人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.医学成像医学成像口腔健康口腔健康舌头上的病变舌头上的病变

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