皮肤学知情深度学习模型用于分类行为性角质炎和皮肤状细胞癌
Diego A Ramos-Briceño1,2, Juan Pinto-Cuberos3, Anthony Linfante3
1Luis Razetti School of Medicine, Universidad Central de Venezuela, Caracas, Venezuela. diegormsb@gmail.com.
Scientific reports
|December 3, 2025
概括
这项研究引入了一种使用深度学习的AI模型,以精确区分皮肤状细胞癌 (cSCC) 和皮肤状细胞癌 (AK) 的皮肤镜像. 人工智能实现了高精度,帮助皮肤科医生进行诊断.
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:
- 精确区分行为性角质 (AK) 和皮肤状细胞癌 (cSCC) 对于治疗至关重要.
- 组织病理学是黄金标准,但不切实际;皮肤透视有诊断的局限性.
- 现有的AI模型往往忽略了临床推理至关重要的关键皮肤学特征.
研究的目的:
- 开发和评估基于CNN的AI模型,用于使用皮肤镜图像在现场对AK和cSCC进行分类.
- 将临床相关的预处理步骤集成到AI模型中,以提高诊断性能.
- 提高皮肤科医生在区分这些皮肤病变时的诊断信心和精度.
主要方法:
- 使用EfficientNetB0开发了一种双分支CNN模型,用于RGB图像,以及一个单独的分支用于增强的血管和角质化模式.
- 利用了2000张皮肤镜像的数据集,增加到近20万个训练实例.
- 在10个代中使用重复的保留验证来进行模型评估.
主要成果:
- 性能最好的模型实现了98.61%的精度,98.33%的灵敏度和98.90%的特异性.
- 精度和F1分数也很高,分别为98.90%和98.61%,低损耗为0.3120.
- 结果超过了以前的模型,突出了结合临床知情预处理的好处.
结论:
- 整合临床知情预处理显著提高了CNN在AK/cSCC分类中的表现.
- 这种人工智能方法支持皮肤科医生,在区分AK和cSCC时提供了更大的信心和精度.
- 该模型代表了朝着皮肤病学中临床一致的AI系统迈出的一步.
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