基于深度学习的严重程度分类,使用日本患者的标准化面部图像
Keiko Watanabe1, Kimi Iinuma2, Chisa Nakashima2
1Department of Dermatology, Kindai University Nara Hospital, Nara, JPN.
Cureus
|October 13, 2025
概括
一个新的深度学习模型使用面部图像准确地对日本患者的严重程度进行了分类. 这种工具有助于客观评估和及时治疗,可能减少痕和后炎性超色素 (PIH).
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 评估的严重程度对于有效治疗至关重要.
- 现有的深度学习模型往往缺乏针对不同族裔皮肤类型的特异性,比如日本人的皮肤,容易发生炎症后多颜色化 (PIH).
研究的目的:
- 开发和验证一个深度学习模型,用于日本患者的严重程度的自动分类.
- 提供一个客观的工具,支持对的诊断和治疗决策.
主要方法:
- 在349张日本患者的面部图像上训练了一个EfficientNet-B2模型.
- 使用了两阶段的课程学习策略,班级加权和数据增强.
- 通过使用调查员的全球评估 (IGA) 评分 (0-4) 评估持久测试组的评估性能.
主要成果:
- 获得了90.0%的整体准确率和0.885的宏观平均F1分数.
- 对于严重类 (IGA-3和IGA-4) 的完美回忆 (1000).
- 获得了0.998.99的曲线下的宏观平均接收器运行特征面积 (ROC-AUC).
结论:
- 在日本数据集上训练的深度学习模型准确地分类了的严重程度.
- 这种工具可以标准化临床评估,并支持及时治疗,潜在地减轻PIH和痕风险.
关键词:
的分级 的分级一般性 (acne vulgaris) 是一种二元分类是二元分类中的一种.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.有效的net-b2 有效的net-b2调查人员的全球评估得分.日本的数据集.更多相关视频
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