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增强足部健康:利用适应式加权子梯度卷积神经网络进行糖尿病足部的分类

Abdullah Alqahtani¹, Shtwai Alsubai², Mohamudha Parveen Rahamathulla^3,4

¹Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|September 9, 2023

概括

糖尿病足 (DFU) 的分类通过使用一种新的自适应加权亚梯度卷积神经网络 (AWSg-CNN) 进行了改进. 这种深度学习方法提高了精度,以更好地治疗DFU和预防截肢.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
计算机科学计算机科学

背景情况:

糖尿病足 (DFU) 是一个重大的全球健康挑战,需要迅速和准确的诊断,以防止截肢等严重并发症.
对DFU的临床评估通常是复杂和耗时的.
深度学习 (DL) 为提高DFU分类准确性提供了有希望的功能.

研究的目的:

引入和评估一个自适应加权亚梯度卷积神经网络 (AWSg-CNN) 以提高DFU分类.
通过先进的深度学习技术,提高DFU诊断的准确性和效率.

主要方法:

该研究使用了DFUC数据集,首先进行数据预处理以消除不一致性并提高数据质量.
一个新的AWSg-CNN模型被用于DFU分类,结合了随机初始化权重 (RIW) 和日志软max与自适应子梯度优化器 (ASGO).
RIW促进了特征空间学习,日志软max防止了梯度下流,ASGO控制了梯度步骤以优化学习速度.

主要成果:

与传统方法相比,拟议的AWSg-CNN方法在分类DFU方面表现优越.
该系统实现了高精度,回忆,F1得分和精度,证实了其有效性.
结果通过使用HTML,CSS和Flask框架的Web界面可视化.

结论:

该AWSg-CNN模型为DFU分类提供了一个强大而准确的解决方案.
这种深度学习方法有可能在糖尿病足的早期检测和治疗方面发挥重要作用.
开发的系统为医疗保健专业人员在管理DFU患者方面提供了实用工具.

关键词:

适应式子梯度优化器适应式子梯度优化器卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.糖尿病足糖尿病足权重的随机初始化权重的随机初始化

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