皮肤-CAD:可解释的深度学习分类皮肤癌从皮肤镜图像通过特征选择的双高层CNNs特征和转移学习的特征
1Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandri, 21937, Egypt; Wearables, Biosensing, and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 21937, Egypt.
Computers in biology and medicine
|June 26, 2024
概括
这项研究介绍了Skin-CAD,这是一个可解释的AI系统,用于从皮肤镜图像中分类皮肤癌 (SC). 它实现了高精度,帮助皮肤科医生在早期和精确的SC检测.
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:
- 皮肤癌 (SC) 构成了全球卫生挑战,需要通过皮肤镜成像进行早期诊断.
- 目前的深度学习计算机辅助诊断 (CAD) 模型缺乏透明度,阻碍了皮肤科医生的信任.
- 可解释性AI (XAI) 对于可解释和可验证的诊断工具至关重要.
研究的目的:
- 开发一种可解释的基于人工智能的CAD系统 (Skin-CAD),用于对皮肤癌的皮肤镜像进行分类.
- 通过为皮肤科医生提供可解释的预测,增强诊断信心.
- 准确地将皮肤病变分为良性或恶性,并分为七个SC子类.
主要方法:
- 皮肤CAD集成了来自多个卷积神经网络 (CNN) 的功能,在双深层 (共享和完全连接) 进行.
- 主要组件分析 (PCA) 减少了聚合层特征的维度,简化了训练.
- 局部可解释的模型不可知解释 (LIME) 为模型预测提供视觉解释.
主要成果:
- 皮肤CAD在皮肤癌:恶性与良性数据集上实现了97.2%的最大精度.
- 该系统在HAM10000数据集上达到96.5%的准确性.
- 特性选择和多CNN双层特征的整合简化了分类.
结论:
- 皮肤CAD为皮肤癌分类提供了一个透明和准确的AI解决方案.
- 该系统的可解释性增强了皮肤科医生的信任,并有助于准确,快速的诊断.
- 皮肤CAD显示出在临床实践中支持皮肤科医生的巨大潜力.
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