一个用于皮肤癌的模型,使用组合学习和深度学习的组合
Mehdi Hosseinzadeh1,2, Dildar Hussain3, Firas Muhammad Zeki Mahmood4
1Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
PloS one
|May 31, 2024
概括
这项研究通过使用机器学习和深度学习模型来增强皮肤癌的检测. 先进的特征选择技术提高了诊断准确度,以区分良性与恶性皮肤病变.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 计算机科学 计算机科学
背景情况:
- 皮肤癌是全球最常见的癌症,每年在美国诊断出数百万例.
- 晚期皮肤癌阶段显著降低了生存率.
- 准确区分良性和恶性皮肤病变对于有效治疗至关重要.
研究的目的:
- 开发和评估机器学习和深度学习模型,以改善皮肤癌诊断.
- 通过整合先进的特征提取和选择方法来提高诊断性能.
- 帮助医疗保健专业人员区分良性和恶性皮肤癌病例.
主要方法:
- 雇员转移学习模型 (DenseNet-201) 作为特征提取器.
- 实现了一个特征选择层,使用包括Lasso,PCA和Random Forest在内的技术.
- 利用机器学习分类器 (MLP,XGB,RF,NB) 和组合方法进行模型优化.
主要成果:
- 实现了 87.72% 的准确率.
- 获得了92.15%的灵敏率.
- 证明了皮肤癌分类的综合特征提取和选择的有效性.
结论:
- 使用DenseNet-201和Lasso特征选择的拟议方法显著改善了皮肤癌诊断指标.
- 机器学习和深度学习技术在帮助皮肤病变的准确分类方面表现有前途.
- 进一步的研究可以完善这些方法,以便在皮肤病学中临床应用.
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