通过转移学习进行增强的皮肤烧伤评估:人类组织分析的新框架
Madhur Nagrath1, Ashutosh Kumar Sahu1, Nancy Jangid1
1CSE, SOET, The NorthCap University, Gurugram, India.
Journal of medical engineering & technology
|March 22, 2024
概括
本研究引入了一种转移学习方法,使用深度学习 (DL) 模型准确评估图像中的烧伤严重程度. VGG-16模型达到97.43%的准确性,改进了基本的卷积神经网络 (CNN) 模型用于烧伤评估.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 皮肤病学 皮肤病学
背景情况:
- 视觉检查是烧伤评估的标准,但可能会错过微妙的烧伤严重程度.
- 不准确的烧伤严重程度评估可能导致治疗延迟,痕,器官衰竭和死亡.
- 传统的临床方法在早期,有效预测烧伤严重程度方面扎.
研究的目的:
- 解决人工智能研究中关于转移学习的差距,以评估烧伤严重程度.
- 用转移学习来提高机器学习模型在分类燃烧度方面的性能.
- 开发一种计算机辅助诊断工具,以便快速准确地评估烧伤损伤.
主要方法:
- 使用深度学习 (DL) 模型的转移学习技术,包括基本的卷积神经网络 (CNN) 和七种不同的转移学习模型.
- 将烧伤图像分为第一,第二和第三度烧伤,以及健康的皮肤,使用完全连接的前神经网络.
- 在Kaggle 2022和马哈拉施特拉理工学院开放学校医学存储库的综合数据集上训练和测试模型.
主要成果:
- 基本的CNN模型实现了93.87%的准确性.
- VGG-16转移学习模型的准确率最高,为97.43%.
- 而DenseNet121模型紧随其后,准确度为96.66%.
结论:
- 拟议的基于CNN的方法,包括转移学习,与基本的CNN模型相比,大大提高了烧伤严重程度评估的准确性.
- 这种人工智能驱动的方法可以帮助医疗保健专业人员及时准确地评估烧伤损伤,从而改善治疗结果.
- 这项研究强调了转移学习在提高机器学习模型性能,用于医疗图像分析在烧伤护理的有效性.
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