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一种基于深度ResNet和转移学习的几次射击糖尿病足图像分类方法
Cheng Wang1,2,3, Zhen Yu4, Zhou Long5
1Shandong Academy of Intelligent Computing Technology, Shandong Institutes of Industrial Technology (SDIIT), Jinan, 250000, China. wangcheng01@ict.ac.cn.
Scientific reports
|December 2, 2024
概括
本研究介绍了一种几次射击的深度学习方法,用于使用ResNet和转移学习对糖尿病脚 (DFU) 的分类. 该方法显著提高了分类准确性,为临床环境提供了高效的辅助诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 糖尿病学 糖尿病学
背景情况:
- 糖尿病足 (DFU) 是糖尿病的严重并发症,可能导致截肢.
- 目前手动的DFU分类是耗时的,缺乏准确性.
- 准确和早期的DFU分类对于有效的患者治疗和管理至关重要.
研究的目的:
- 为糖尿病足 (DFU) 开发一个几次拍摄的图像分类方法.
- 为了利用深度残余神经网络 (ResNet) 和转移学习用于DFU分类.
- 为了应对有限的临床DFU图像数据的挑战.
主要方法:
- 采用数据增强技术 (几何变换,随机噪声) 来扩展DFU数据集.
- 进行了各种深度ResNet模型 (ResNet18,ResNet34,ResNet50,ResNet101,ResNet152) 的比较实验.
- 通过对增强的DFU数据集上微调预先训练的ResNet模型来利用转移学习.
主要成果:
- 增强的数据集大小从146个图像增加到3000个图像,将平均DFU分类准确度从0.9167提高到0.9867.
- 在测试的模型中,ResNet50实现了最高的平均精度 (0.9901) 和最低的损失 (0.1356).
- 最终的DFU分类模型,使用预先训练的ResNet50进行训练,达到0.9867.7的平均精度.
结论:
- 提出的少数拍摄的DFU图像分类方法显示了高精度.
- 深度ResNet和转移学习方法对于对DFU严重性的分类是有效的.
- 该方法适用于低成本,低计算能力的终端设备,有助于临床DFU诊断.
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