多模式皮肤病变分类用于早期癌症诊断,使用深度学习
Vandit Gabani1, T M Navamani1, K Shyamala1
1School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.
Frontiers in physiology
|February 25, 2026
概括
结合DenseNet-201,VGG16和InceptionV3的整体模型在通过皮肤镜图像检测皮肤癌时取得了97.9%的准确性. 这种深度学习方法增强了早期诊断,改善了患者的治疗结果.
科学领域:
- 皮肤病学和人工智能研究
- 医学图像分析 医学图像分析
- 计算病理学计算病理学
背景情况:
- 皮肤癌,特别是黑色素瘤,是全球重要的健康问题.
- 早期发现皮肤癌可以大大提高存活率和治疗效率.
- 传统的诊断方法可能是资源密集型的,依赖于专家的解释.
研究的目的:
- 开发和评估一个深度学习模型,以准确有效地检测皮肤癌.
- 利用深层卷积神经网络 (DCNNs) 来从皮肤镜像中分类皮肤病变.
- 为了提高诊断准确度,并协助皮肤科医生早期识别皮肤癌.
主要方法:
- 使用HAM10000数据集,包括皮肤病变的皮肤镜像.
- 采用了三种DCNN架构:DenseNet-201,VGG16和InceptionV3.3 这三种架构.
- 实现了预处理,微调策略,模型组合和Grad-CAM以实现可解释性.
主要成果:
- 总体模型实现了97.9%的测试精度.
- 该模型表现出卓越的性能,F1得分,回忆和精度为99.2%.
- 整体方法在皮肤病变分类方面表现优于单个DCNN模型.
结论:
- 集成深度学习模型在自动化皮肤病变检测方面表现出高效.
- 开发的模型为早期和准确的皮肤癌诊断提供了一个有前途的工具.
- 通过Grad-CAM增强的模型解释性增加了临床适用性和信任.
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