基于纹理的分类,以克服COVID-19和病毒性肺炎之间的不确定性,使用机器学习和深度学习技术
Omar Farghaly1, Priya Deshpande1
1Data-Intensive Computing Distributed Systems Laboratory, Department of Electrical and Computer Engineering, Marquette University, Milwaukee, WI 53233, USA.
Diagnostics (Basel, Switzerland)
|May 24, 2024
概括
一个新的AI模型使用先进的纹理分析和深度学习准确地对COVID-19和肺炎进行胸部X射线分类. 这种方法提高了诊断准确度,特别是在复杂的病例中,优于传统方法.
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:
- COVID-19的症状模仿病毒性肺炎,使早期诊断复杂化.
- 由于高维图像数据和先前研究的局限性,对COVID-19的准确分类具有挑战性.
- 年轻人,老年人和免疫力低下的人群尤其容易受到伤害.
研究的目的:
- 为准确的胸部X射线图像分析开发一种新的分类模型.
- 为了区分正常的COVID-19和病毒性肺炎病例.
- 为了克服以前研究的局限性,使用简单的算法和小型数据集.
主要方法:
- 集成先进的纹理特征提取方法:灰色水平共发生矩阵 (GLCM),灰色水平依赖矩阵 (GLDM) 和波形变换.
- 图像分类的深度学习框架中的应用.
- 利用每个数据集类固有的独特纹理特征.
主要成果:
- 与传统方法相比,更高的分类性能.
- 高精度 (DLNN:0.92),回忆 (DLNN:0.93),精度 (DLNN:0.87),和F1-Score (DLNN:0.89) 由深度学习神经网络 (DLNN) 实现的.
- 即使使用复杂和多样化的图像数据,也证明了有效性.
结论:
- 拟议的模型代表了基于AI的COVID-19和肺炎诊断系统的重大进步.
- 该方法提供了改善的患者结果和医疗保健管理策略.
- 先进的纹理分析与深度学习相结合,提高了呼吸系统疾病的诊断能力.
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