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智能灰狼神经网络 (MGWONET):改变了糖尿病足的分析
Lavanya Gangadharan1, Karpagam Vilvanathan2
1Department of Biomedical Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India.
Journal of wound care
|October 10, 2025
概括
一个新的深度学习模型MGWONET使用自动化超参数优化准确检测糖尿病足 (DFU). 这提供了一个可靠的远程诊断工具,以支持早期干预和预防截肢.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 糖尿病足 (DFU) 构成下肢截肢的重大风险.
- 目前的DFU诊断通常是昂贵的,耗时的,需要亲自临床评估.
- 对于可疑的DFU,需要自动化,远程诊断解决方案.
研究的目的:
- 推出MGWONET,这是一种用于自动化DFU分类的新型深度学习模型.
- 在深度学习模型中使用修改的灰狼优化 (MGWO) 算法进行自动超参数调整.
- 开发一种可靠和有效的方法,在DFU诊断中区分健康和性皮肤.
主要方法:
- 开发了MGWONET,这是一个集MGWO算法用于超参数优化的深度学习模型.
- 在2200张DFU图像的增强数据集上训练MGWONET用于二进制分类 (健康与).
- 使用标准指标评估模型性能,包括准确性,回忆,精度,特异性,平衡分类率和ROC曲线分析.
主要成果:
- 最优的MGWONET模型实现了98.64%的分类准确度.
- MGWONET的性能优于已有的深度学习架构 (AlexNet,VGG16,GoogleLeNet) 和一个基线灰狼优化模型.
- 该模型在基于二进制图像的分类中展示了强大的区分能力.
结论:
- MGWONET为自动化DFU诊断提供了一个高度可靠的框架.
- 该模型有可能在临床实践中成为一个有价值的支持工具.
- MGWONET可以帮助减少诊断负担,并通过智能健康服务促进早期干预.
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