基于层融合的深度学习方法用于皮肤癌分类的多阶段知识蒸
Mahir Afser Pavel1, Ramisa Asad1, Goh Kah Ong Michael2
1Electrical and Computer Engineering, North South University, Dhaka, 1229, Bangladesh.
Scientific reports
|November 13, 2025
概括
这项研究引入了用于皮肤癌分类的深度学习方法,达到95.88%的准确性. 该技术通过提高模型性能和减少处理时间来增强早期诊断.
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:
- 皮肤癌是全球普遍存在的健康问题,需要在诊断工具方面取得进展.
- 早期检测和皮肤病变的准确分类对于有效的治疗和改善患者的结果至关重要.
研究的目的:
- 开发和评估一种新的多阶段知识蒸深度学习技术,用于分类皮肤病变细胞.
- 提高皮肤癌诊断模型的准确性,效率和可解释性.
主要方法:
- 利用HAM10000数据集与增强和预处理进行强大的模型训练.
- 实施了多阶段知识蒸方法,使用混合教师模型 (ViT和ConvNeXT) 和蒸模型 (CNN和EfficientNet).
- 嵌入层融合,中间出口,训练后定量化,以及可解释的AI (XAI) 技术 (Grad-CAM,Score-CAM,LIME).
主要成果:
- 优化的蒸模型实现了高性能指标:95.88%的准确性,95.91%的F1分数和99.02%的AUC.
- 显著减少了训练和推断时间 (分别为61毫秒/步和15毫秒/步).
- 通过应用多种XAI技术,提高了模型的可解释性.
结论:
- 拟议的多阶段知识蒸与层融合提供了皮肤病变分类准确性和效率的显著改善.
- 优化和量化模型为皮肤癌诊断提供了计算效率高,可解释的解决方案.
- 该研究提供了可访问的代码和量子化模型,用于进一步的皮肤癌检测研究和应用.
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