使用改进的高效NetV2L与自适应性早期停止机制进行增强的皮肤癌分类
Chandrasekar Venkatachalam1, Shanmugavalli Venkatachalam2,2, Arunkumar Balakrishnan3,3
1Department of Computer Science and Engineering, Faculty of Engineering, Jain (Deemed to be university), Bangalore, 562112, Karnataka, India.
Scientific reports
|November 3, 2025
概括
一个新的EfficientNetV2L模型准确地分类皮肤癌类型,达到99.22%的准确性. 这种先进的深度学习方法克服了过度匹配和数据集不平衡,以可靠地诊断皮肤病变.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 准确的皮肤癌分类对于医学诊断至关重要.
- 深度学习模型,特别是卷积神经网络 (CNN),用于皮肤病变分析.
- 现有的CNN模型与一般化,过度拟合和不平衡的数据集作斗争,限制了临床应用.
研究的目的:
- 开发一个强大而高效的深度学习模型,用于准确的皮肤病变分类.
- 解决传统的CNN在处理多样化和不平衡的皮肤病变数据集方面的局限性.
- 提高模型的概括能力,用于皮肤癌诊断中未见的数据.
主要方法:
- 提出了一种利用EfficientNetV2L架构进行皮肤病变分类的新型模型.
- 实施了适应性早期停止和学习率回调,以增强模型概括性和防止过度拟合.
- 在公开可用的国际皮肤成像协作 (ISIC) 数据集上训练和评估模型.
主要成果:
- 基于EfficientNetV2L的模型在ISIC数据集上实现了高分类准确率99.22%.
- 在分类各种类型的皮肤病变方面表现出强度.
- 适应性回调有效地提高了概括性,并减轻了过拟合问题.
结论:
- 开发的EfficientNetV2L模型为皮肤癌的自动诊断提供了强大而高效的解决方案.
- 拟议的方法提高了模型可靠性和临床相关性,用于区分皮肤病变.
- 这项工作有助于推进皮肤病学中人工智能驱动的医学诊断领域.
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