浅层和深度学习方法用于分类黑色素瘤和非黑色素细胞皮肤病变
Newton Spolaôr1, Huei Diana Lee1, Weber Shoity Resende Takaki1
1Laboratory of Bioinformatics (LABI), Graduate Program in Electrical Engineering and Computer Science (PGEEC), Western Paraná State University (UNIOESTE), Presidente Tancredo Neves Avenue, 6731, 85867-900 Foz do Iguaçu, Brazil.
Medical engineering & physics
|February 5, 2026
概括
这项研究比较了深度学习和浅层学习来分类皮肤病变,在检测恶性黑色素瘤方面实现了高精度. 这些计算系统可以帮助皮肤科医生早期发现皮肤癌.
科学领域:
- 皮肤病学 皮肤病学
- 医学图像分析 医学图像分析
- 人工智能的人工智能
背景情况:
- 皮肤病学中的图像处理帮助卫生专家诊断皮肤病变.
- 黑色素瘤是一种恶性黑色素细胞状况,需要准确及时检测.
- 浅层和深度学习方法越来越多地用于皮肤学图像分析.
研究的目的:
- 为了比较浅层和深度学习方法来分类恶性黑色素细胞病变与非黑色素细胞病变.
- 开发和评估用于皮肤病变分类的新型深度神经网络配置.
- 评估辅助程序的影响,如过量采样,特征选择和数据增强.
主要方法:
- 开发了39种学习方法配置,包括三种新型微调深度神经网络.
- 实施了辅助程序,如过量采样,特征选择和数据增强.
- 使用公共Derm7pt皮肤镜数据库进行实验评估,并进行了分层嵌套交叉验证.
主要成果:
- 最好的原创深度学习设置实现了与现有文献相比的竞争性表现.
- 获得了0.9909的平均准确度和0.9976的恶性黑色细胞病变分类的灵敏度.
- 在所有39个配置中都超过了大多数类错误基线.
结论:
- 新的深度学习配置显示了准确的皮肤病变分类的前景.
- 这些计算系统可以作为初步过器来支持早期皮肤癌检测.
- 这些发现可以激励皮肤科医生开发先进的决策支持工具.
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