用基于深度学习的方法对皮肤疾病进行分类
Merve Okumuş Sarı1, Kübra Keser2
1Simav Faculty of Technology, Department of Electrical and Electronics Engineering, Kutahya Dumlupinar University, Simav, 43500, Kutahya, Turkey.
Scientific reports
|July 28, 2025
概括
这项研究表明,支持矢量机 (SVM) 分类与缓解算法相结合,在区分湿疹,皮脂性皮肤炎和皮肤癌方面取得了高准确性,从而改善了早期诊断和治疗.
科学领域:
- 皮肤病学 皮肤病学
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 皮肤疾病如湿疹,皮脂性皮肤炎和皮肤癌显著影响生活质量.
- 准确和及时的诊断对于有效管理和治疗这些疾病至关重要.
- 目前的诊断方法可能需要提高速度和准确性,以便进行大规模分类.
研究的目的:
- 开发和评估一种机器学习模型,用于准确分类三种常见的皮肤疾病:湿疹,斑疹性皮肤炎和皮肤癌.
- 为了比较不同分类算法的性能,包括AlexNet和Support Vector Machine (SVM) 与Relief算法.
- 评估使用浮雕算法在提高分类准确性的特征选择的有效性.
主要方法:
- 使用了包含693名湿疹患者,750名皮肤癌患者和770名斑疹性皮肤炎患者的数据集.
- 采用Relief算法进行特征选择,以提高分类性能.
- 实施并将AlexNet和SVM与使用交叉验证进行皮肤疾病分类的Relief算法进行比较.
- 在2017年ISIC数据集上评估模型性能,培训和测试数据分区不同 (80/20和70/30).
主要成果:
- 使用Relief算法的SVM分类实现了92.10%的更高准确率,与AlexNet (89.39%) 相比,SVM分类具有80%的培训和20%的测试分割.
- 在ISIC 2017数据集上,SVM与Relief算法实现了89.16%的准确性 (80/20分割) 和91.11%的准确性 (70/30分割).
- 综合特征选择和简化架构的拟议模型表现出卓越的性能,突出了深度学习和转移学习在皮肤疾病分类中的有效性.
结论:
- 与Relief算法相结合的SVM分类是一种高度准确和有效的方法,用于分类湿疹,脂质性皮肤炎和皮肤癌.
- 这项研究证实了深度学习和转移学习技术在早期和精确诊断皮肤疾病方面的潜力.
- 通过及时有效的治疗,提高分类准确度可以降低皮肤癌死亡率.
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