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皮肤疾病诊断使用决策和特征水平融合深层特征的特征
Md Zasim Uddin1, Md Arif Shahriar1, Björn W Schuller2,3
1Department of Computer Science and Engineering, Begum Rokeya University, Rangpur, Bangladesh.
Frontiers in digital health
|November 3, 2025
概括
这项研究引入了一种混合组合框架,用于自动化皮肤疾病分类. 决策级融合 (DLF) 与特征级融合 (FLF) 相比,表现优越,提高了诊断准确度.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 手动皮肤病诊断是主观和不一致的.
- 使用机器学习的自动化方法显示出准确检测的前景.
- 深度学习模型为图像分析提供了先进的功能.
研究的目的:
- 为改善皮肤病检测和分类开发混合组合框架.
- 整合特征级融合 (FLF) 和决策级融合 (DLF) 战略.
- 利用卷积神经网络 (CNN) 和视觉转换器 (ViT) 模型的互补优势.
主要方法:
- 提出了一个混合组合框架,将FLF和DLF结合起来.
- 使用了DenseNet201,VGG19和视觉变压器 (ViT) 模型.
- 集成的生成对抗网络 (GAN) 用于数据增强.
主要成果:
- 该框架在四个基准数据集 (PH2,HAM10000,ISIC 2018,ISIC 2019) 中实现了高精度.
- 决策级融合 (DLF) 的表现略高于特征级融合 (FLF).
- 精度在86.7%至99.3%之间,取决于数据集和融合方法.
结论:
- 拟议的混合组合框架显著改善了皮肤疾病的分类.
- 决策层面的融合是一个有价值的战略,可提供强大而准确的诊断结果.
- 组合方法提高了自动皮肤病诊断的可靠性.
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