根据指甲图像和转移学习技术,对黑色心态,博氏线和指甲俱乐部进行分类
Derya Yeliz Coşar Soğukkuyu1, Oğuz Ata2
1Institute of Graduate Studies, Altinbas University, İstanbul, Turkey.
PeerJ. Computer science
|September 14, 2023
概括
人工智能 (AI) 模型使用图像分析准确诊断常见的指甲疾病. 使用VGGNet的转移学习实现了高精度,有助于早期检测指甲状况.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 爪病是指甲板的形,通常呈现出明显的症状.
- 对于医疗专家来说,对指甲状况的差异诊断可能是一个挑战.
- 爪病的分类依赖于迹象和症状,有时与其他疾病有关.
研究的目的:
- 实施一种新的人工智能 (AI) 模型来诊断常见的指甲疾病.
- 评估转移学习模型在根据图像对指甲疾病进行分类方面的有效性.
- 使用基于AI的方法来确定三种常见的指甲疾病的发生情况.
主要方法:
- 利用公开可用的指甲病数据集进行模型培训和验证.
- 实施了基于使用视觉几何组 (VGGNet) 的转移学习的两个分类模型.
- 采用VGG16和VGG19架构用于从图像中检测和分类指甲疾病.
主要成果:
- 在诊断指甲疾病方面,VGG16模型实现了94%的准确性.
- VGG19模型在指甲疾病分类方面表现出93%的准确率.
- 这两种模型都显示出高精度,表明AI在指甲疾病诊断中的潜力.
结论:
- 利用转移学习的计算机辅助诊断系统可以有效地识别多重损伤指甲疾病.
- 基于人工智能的图像分析显示,对皮肤病的早期和准确诊断有希望.
- 该研究强调了VGGNet模型在治疗指甲病理的医学图像分析中的实用性.
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