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E-DFu-Net:一个高效的深层卷积神经网络模型用于糖尿病足的分类
Nouf F Almufadi1, Haifa F Alhasson1, Shuaa S Alharbi1
1Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
Biomolecules & biomedicine
|September 25, 2024
概括
本研究提出了一种使用转移学习的自动化方法来检测糖尿病足 (DFU),并将其分类为缺血性或感染性. 一个新的模型,E-DFu-Net,实现了高精度,改善了早期检测以防止截肢.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 糖尿病并发症管理 糖尿病并发症管理
背景情况:
- 糖尿病足 (DFU) 影响全球33%的糖尿病患者,经常导致肢体截肢.
- 早期检测和准确分类DFU亚型 (缺血与感染) 是关键的,但具有挑战性.
- 目前对DFU的诊断方法可能是主观的和耗时的.
研究的目的:
- 开发和评估一种自动化的方法来识别和分类使用转移学习的糖尿病足.
- 为了比较各种预训练的深度卷积神经网络 (DCNN) 模型的性能,用于DFU检测.
- 引入一个新的DCNN模型,E-DFu-Net,旨在改善DFU分类和减少过.
主要方法:
- 使用七个预训练的深度卷积神经网络 (DCNN) 模型进行转移学习:EfficientNetB0,DenseNet121,ResNet101,VGG16,MobileNetV2,InceptionV3和InceptionResNetV2.2.
- 通过调整现有的DCNN架构来开发一种新型模型,即E-DFu-Net,以提高DFU分类的准确性并防止过度装配.
- 评估了DFU数据集上的模型性能,重点是区分缺血性和感染性状态的准确性.
主要成果:
- 拟议的E-DFu-Net模型在DFU分类中表现出卓越的性能.
- 在分类缺血性糖尿病足方面取得了97%的准确性.
- 获得了92%的准确性来分类感染的糖尿病足.
结论:
- 使用转移学习的自动 DFU 检测和分类,特别是使用 E-DFu-Net 模型,显著提高了诊断能力.
- 开发的方法为早期和准确识别DFU亚型提供了一个有希望的工具,可能降低截肢率.
- 这一进步支持医疗保健从业者,提供一种可靠的方法来管理糖尿病足并发症.
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