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通过整体方法提高皮肤癌诊断准确度

Priya Natha¹, Sivarama Prasad Tera², Ravikumar Chinthaginjala³

¹Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India.

Scientific reports

|January 8, 2025

概括

这项研究引入了一种优化的Max Voting组合方法,用于改进皮肤癌症分类. 该方法结合了多种机器学习模型和遗传算法,实现了高精度的早期和精确的诊断.

科学领域:

皮肤病学和医学成像学
医疗保健中的人工智能
计算病理学计算病理学

背景情况:

早期和准确的皮肤癌诊断对于有效的治疗至关重要.
当前的诊断方法可能不一致,需要改进预测建模.
机器学习 (ML) 模型显示了自动化皮肤病变分析的前景.

研究的目的:

通过整体ML方法提高皮肤癌分类的准确性.
介绍和评估一种用于优化预测的新型Max Voting方法.
整合遗传算法 (GA) 以实现最佳的特征向量生成.

主要方法:

训练并评估随机森林 (RF),多层感知神经网络 (MLPN) 和支持矢量机器 (SVM) 模型.
实施了Max Voting组合技术,将来自单个模型的预测结合起来.
利用遗传算法 (GA) 来优化从图像数据中获得的特征向量.

主要成果:

与单个模型相比,Max Voting组合方法显著改善了整体性能.
在HAM10000和ISIC 2018数据集上实现了94.70%的分类准确度.
马克斯投票方法产生了卓越的F1测量,回忆和精度结果.

结论:

马克斯投票方法为皮肤癌病变的分类提供了一种可靠和强大的方法.
通过Max Voting将多个ML模型结合起来,可以利用个人的优势来提高预测性能.
这种优化的组合技术代表了皮肤学图像分析的新和高效的进步.

关键词:

组合模型模型组合模型在HAM10000数据集中,我们使用的是HAM10000数据集.在ISIC 2018数据集中.机器学习 (ML) 是指机器学习.最大投票方法投票方法多种类别的皮肤癌分类.多层感知神经网络 (MLPN) 是一种多层感知神经网络.随机森林 (RF) 是一个随机的森林.支持矢量机器 (SVM) 是一个支持矢量机器.

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