整合多源数据用于使用深度学习进行皮肤烧伤分类
Ahmed Elsarta1, Habiba Fathalla1, Marina Nasser1
1Department of Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Giza, Canada.
Computers in biology and medicine
|June 25, 2025
概括
本研究引入了一种人工智能方法,用于分类烧伤严重程度和确定接种需求,实现高精度的接种分类. 开发的人工智能模型显示了改善烧伤评估中的临床决策支持的前景.
科学领域:
- 医疗人工智能 医疗人工智能
- 皮肤病学 皮肤病学
- 计算机视觉 计算机视觉
背景情况:
- 准确的烧伤程度分类对于有效的治疗和临床决策至关重要.
- 对非专业人士来说,视觉烧伤评估具有挑战性,需要人工智能辅助工具.
- 现有的烧伤人工智能模型在数据多样性,偏见和标准化方面存在局限性.
研究的目的:
- 开发一种基于人工智能的系统,用于分类烧伤程度和确定接种要求.
- 通过改进的数据集和方法来解决当前AI燃烧评估工具的局限性.
- 加强对烧伤管理的临床决策支持.
主要方法:
- 开发了一个强大的数据管道,以多样化,注释图像增强数据集,包括特别关注埃及肤色.
- 使用深度学习模型 (ResNet50,DenseNet,MobileNet,VGG16,ShuffleNet) 来进行图像分类.
- 使用级联分类器来评估烧伤程度,并使用二进制分类来确定接种.
主要成果:
- 一个经过修改的ResNet50模型实现了94.03%的准确度和0.94 F1分数的接种分类,超过其他模型.
- 级联分类器实现了63.23%的准确性和0.63 F1分数,用于烧伤程度的分类.
- 通过在各种临床环境中使用多样化,精心策划的数据集来证明深度学习的有效性.
结论:
- 人工智能模型显示了皮肤烧伤分类和临床决策支持的巨大潜力.
- 开发的方法结合了多样化的数据集和新的方法,为燃烧评估推进了标准化的AI.
- 深度学习模型,当在高质量,多样化的数据上训练时,可以导致更可靠的燃烧评估工具.
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