模糊格子辅助EJAYA Q学习用于自动化肺部疾病分类
Amit Kukker1, Rajneesh Sharma2, Gaurav Pandey2
1Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Ghaziabad, U.P., 201204, India.
Biomedical physics & engineering express
|August 23, 2024
概括
这项研究引入了增强的JAYA (EJAYA) 辅助Q-学习,用于从胸部X射线分类肺部疾病. 这种新方法使用模糊格子和施罗丁格方程准确识别肺炎和结核病 (TB),提供更快的诊断工具.
科学领域:
- 医学成像和诊断 医学成像和诊断
- 医疗保健中的人工智能
- 量子力学应用 量子力学应用
背景情况:
- 对肺炎和结核病 (TB) 等肺部疾病进行准确和快速的分类对于有效的患者管理至关重要.
- 现有的诊断方法可能会面临实时,非线性和非静止数据的挑战.
- 自动化分类系统可以提高诊断速度和准确性,特别是在资源有限的环境中.
研究的目的:
- 为了提出和评估一种新技术,Enhanced JAYA (EJAYA) 协助Q-Learning,使用胸部X射线图像对肺部疾病进行分类.
- 开发使用模糊格子和施罗丁格方程来处理复杂的实时数据的特征提取方法.
- 评估拟议方法在分类肺炎 (正常,轻度,严重) 和结核病 (存在,不存在) 的性能.
主要方法:
- 使用从胸部X射线像素形成的模糊格子进行特征提取,使用动能 (K.E.) 通过施罗丁格方程计算.
- 使用Q学习进行自适应分类,通过EJAYA优化算法选择最佳的Q值.
- 使用具有最高 K.E. 的特征向量格子. 作为分类器的输入.
主要成果:
- 对于肺炎的分类 (3000张图像):准确率为97.90%,灵敏度为98.43%,特异性为97.25%,精度为97.78%,F值为98.10%.
- 对于结核病检测 (600个样本):准确率为95.50%,灵敏度为96.39%,特异性为94.40%,精度为95.52%,F值为95.95%.
- 肺炎的分类时间为40.96秒,结核病的分类时间为39.98秒,与当代方法相比,其速度和准确性更高.
结论:
- 拟议的EJAYA辅助Q-Learning技术提供了一个快速而准确的自动化工具,用于从胸部X射线图像中分类肺炎和结核.
- 模糊格子和基于施罗丁格方程的特征提取的集成有效地处理复杂的实时医疗数据.
- 该方法显示了改善肺病检测诊断工作流程的巨大潜力.
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