基于CT扫描图像和多变异性肺呼吸道疾病的COPD早期诊断和分类的新型多式框架
Santosh Kumar1, Vijesh Bhagat1, Prakash Sahu1
1Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
这项研究引入了一个新的多式模式框架,用于早期慢性阻塞性肺病 (COPD) 诊断,使用CT扫描和肺声音. 该模型实现了高精度,改善了早期检测和预测COPD严重程度.
科学领域:
- 医学成像和机器学习
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性阻塞性肺病 (COPD) 的诊断是具有挑战性的,因为错误的分类和耗时的方法.
- 由于其严重的健康后果,早期发现和准确诊断COPD至关重要.
研究的目的:
- 设计和开发一种用于早期诊断和准确预测COPD的多式联络框架.
- 为了利用计算机断层扫描 (CT) 扫描图像和肺部声音/咳音频样本来检测COPD.
主要方法:
- 从CT图像和音频样本中提取了包括纹理,直方图强度,色彩,MFCC和高斯尺度空间在内的特征.
- 利用无监督的ML进行特征选择和组合学习进行分类和严重性评估.
- 通过使用AIIMS,Raipur和开放呼吸数据集的数据验证了框架.
主要成果:
- 多式联络框架实现了COPD早期诊断的高准确性.
- 具体精度:97.50% (融合技术),98% (CT模型) 和95.30% (咳模型).
结论:
- 拟议的框架证明了COPD的有效早期诊断.
- 性能与现有方法和用于早期发现COPD的传统技术进行了比较.
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