使用SMOTE Tomek和优化的卷积神经网络进行DSSCC净增强皮肤癌分类
Muhammad Aqib Javaid1, Muhammad Suleman Shahzad2, Hafiz Muhammad Faisal Shehzad1
1Department of Software Engineering, University of Sargodha, Sargodha, Pakistan.
Scientific reports
|November 25, 2025
概括
这项研究介绍了DSSCC-Net,这是一种深度学习模型,可以使用皮肤镜图像准确地分类皮肤癌. 它有效地处理数据不平衡,实现早期检测和临床使用的高精度.
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:
- 皮肤癌的诊断依赖于专家的评估,这可能容易出错.
- 目前的深度学习模型在与不平衡的数据集作斗争,阻碍了对罕见皮肤病变的准确分类.
- 需要强大,高效和可解释的AI模型来早期发现皮肤癌.
研究的目的:
- 开发和评估DSSCC-Net,这是一个新的深度学习框架,用于准确地分类皮肤癌.
- 用SMOTE-Tomek过量抽样来解决皮肤学数据集中类不平衡的挑战.
- 为了提高临床应用的分类模型的可解释性.
主要方法:
- 开发了DSSCC-Net,这是一个深层卷积神经网络 (CNN),与SMOTE-Tomek过量采样集成.
- 在各种皮肤镜数据集上训练并验证了模型 (HAM10000,ISIC 2018,PH2).
- 利用数据增强,丢弃层,ReLU激活和Grad-CAM用于模型优化和可解释性.
主要成果:
- DSSCC-Net实现了高的分类准确性 (平均97.82%±0.37%) 和AUC (99.43%).
- 与VGG-16和ResNet-152.2等现有最先进的模型相比,该模型表现出卓越的性能.
- SMOTE-Tomek集成显著改善了少数类检测,具有平衡的精度 (97%) 和回忆 (97%).
结论:
- DSSCC-Net为皮肤癌分类提供了强大的和可解释的解决方案,其性能优于当前的方法.
- 该模型处理阶级不平衡的能力使其适合于现实世界的临床部署.
- DSSCC-Net设置了一个新的基准,为人工智能辅助的早期皮肤癌诊断铺平了道路.
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