基于Gaussian Aquila优化器的双卷积神经网络,用于识别和分类骨关节炎,使用膝关节图像
B Subha1, Vijay Jeyakumar2, S N Deepa3
1Department of Biomedical Engineering, PSNA College of Engineering and Technology, Dindigul, India. subhapsna@gmail.com.
Scientific reports
|March 28, 2024
概括
这项研究介绍了一种新的高斯方优化器-双卷积神经网络 (GAO-DCNN),用于使用X射线图像早期检测骨关节炎 (OA). 该GAO-DCNN实现了高精度,改善了及时的患者治疗.
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:
- 骨关节炎 (OA) 是一种退行性肌肉骨疾病,导致严重的疼痛和残疾.
- 早期发现OA对于及时有效治疗患者至关重要.
- 当前的诊断方法可能会从先进的计算方法中受益.
研究的目的:
- 开发和评估一种新的混合深度学习模型,用于准确的骨关节炎检测和分类.
- 通过使用新的元启发算法引入一个优化的深度学习架构.
- 为了提高膝盖X射线图像OA诊断的效率和准确性.
主要方法:
- 通过将高斯基突变纳入Aquila优化器,开发了一种新的高斯基Aquila优化器 (GAO).
- 一个双卷积神经网络 (DCNN) 被设计成平衡的卷积层和优化的参数.
- 该GAO被用来优化DCNN模型的权重和偏差用于OA分类.
- 采用了2283张膝盖X射线图像 (1267张正常,1016张OA) 的数据集.
主要成果:
- 拟议的GAO-DCNN系统实现了高分类性能.
- 灵敏度: 98.25% 灵敏度: 98.25% 灵敏度: 98.25% 灵敏度: 98.25% 灵敏度:
- 具体性: 98.93% 的特异性.
- 分类准确度:对于异常膝盖病例,分类准确率为98.77%.
- 与现有的深度学习模型相比,混合模型表现出优异的性能.
结论:
- 开发的GAO-DCNN是一种高效和准确的方法,用于从膝盖X射线图像中检测骨关节炎.
- 新的GAO优化器提高了DCNN模型的性能.
- 这种方法为早期骨关节炎诊断提供了一个有前途的工具,有可能减少患者的疼痛并改善治疗结果.
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