通过ESN-MDFS增强多类COVID-19预测:使用平均退学特征选择技术的极端智能网络
Saghir Ahmed1, Basit Raza1, Lal Hussain2,3
1Department of Computer Science, COMSATS University, Islamabad Capital Territory, Islamabad, Pakistan.
PloS one
|November 12, 2024
概括
一个新的AI模型,用ESN-MDFS增强COVID预测,通过胸部X射线准确诊断COVID-19和肺炎等肺部疾病. 这种深度学习方法显著提高了便携式X射线分析的诊断准确性和效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 便携式胸部X射线 (CXR) 对于诊断肺部疾病至关重要.
- 深度学习 (DL) 和人工智能 (AI) 显示出增强CXR分析的潜力.
- 准确区分各种肺部病理,包括COVID-19,细菌和病毒性肺炎,仍然是一个挑战.
研究的目的:
- 开发和评估一种新的AI模型,用于在便携式CXR中检测多类肺部疾病.
- 为了提高诊断准确性和有效性,识别COVID-19,细菌性肺炎,病毒性肺炎和正常病例.
- 为了加强分类,将静态纹理功能与动态深度学习功能相结合.
主要方法:
- 利用了超过6,000张便携式CXR图像的数据集,包括COVID-19,正常,病毒性肺炎和细菌性肺炎病例.
- 开发了"用ESN-MDFS增强COVID预测"模型,集成了极端智能网络 (ESN) 和平均退出特征选择技术 (MDFS).
- 使用预训练的VGG-16模型进行特征提取,结合静态纹理特征,并解决数据不平衡和超参数调整.
主要成果:
- 该ESN-MDFS模型实现了96.18%的峰值精度和1.00的曲线下面面积 (AUC).
- 在区分COVID-19,细菌性肺炎,病毒性肺炎和正常情况方面表现出卓越的表现.
- 六次交叉验证证实了该模型的强大和可靠的诊断能力.
结论:
- 拟议的ESN-MDFS模型在使用便携式CXR进行肺部疾病自动诊断方面取得了重大进展.
- 这种人工智能驱动的方法有望提高诊断的准确性和效率,帮助临床医生及时管理患者.
- 该研究强调了整合DL和AI的潜力,以便在资源有限的环境中更好地检测肺部疾病.
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