云中的定制图像识别模型用于皮肤疾病识别.
Mircea-Aurelian Dan1, Stelian Nicola1, Lacramioara Stoicu-Tivadar1
1Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania.
Studies in health technology and informatics
|October 23, 2023
概括
这项研究开发了一个使用Azure Custom Vision的AI模型来分类太阳晶状体和动态角质炎,达到94%的准确性. 这项技术有助于识别由紫外线辐射和气候变化加剧的常见皮肤疾病.
科学领域:
- 皮肤病学和人工智能的人工智能
- 计算病理学计算病理学
- 医学成像分析 医学成像分析
背景情况:
- 由于全球变暖和紫外线辐射暴露,皮肤疾病的患病率越来越高,如太阳晶状体和动作性角质炎.
- 对常见皮肤病的有效和准确诊断工具的需求.
- 机器学习在医学图像分类中的潜力.
研究的目的:
- 通过使用Azure Custom Vision服务,提出一种技术方法来分类太阳晶状体和动态角质炎.
- 利用基于云的AI来识别皮肤病.
- 为基于图像的诊断开发一个卷积神经网络 (CNN) 模型.
主要方法:
- 利用Azure自定义视觉服务来开发CNN模型.
- 在来自ISIC数据库的600张图像数据集上训练模型.
- 使用手动图像标签来准备数据集.
主要成果:
- 在预先训练的图像上,训练的神经网络的分类性能达到了94%.
- 证明了Azure Custom Vision在生成高性能CNN模型中的有效性,使用相对较小的数据集.
- 成功开发了一种用于分类太阳晶状体和动态角质炎的功能模型.
结论:
- 使用Azure自定义视觉的拟议技术方法对分类太阳晶状体和动态角质炎有效.
- 基于云的AI服务为医疗图像分析提供了显著的计算能力和效率.
- 手动图像标签仍然是开发AI诊断工具的局限性,但高性能是可以实现的.
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