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集成CT扫描数据集的整体分类用于检测COVID-19使用特征融合从轮变换和CNN的特征融合

Md Nur-A-Alam¹, Mostofa Kamal Nasir¹, Mominul Ahsan²

¹Department of Computer Science & Engineering, Mawlana Bhashani Science and Technology University, Tangail, 1902, Bangladesh.

Scientific reports

|November 17, 2023

概括

一种自动化的机器学习和深度学习方法从CT扫描中准确地检测到COVID-19. 这种方法为早期肺病检测提供了高准确度,可能降低死亡率.

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
计算病理学计算病理学

背景情况:

随着COVID-19大流行不断演变,出现新的变种,需要快速准确的诊断工具.
早期发现COVID-19肺部疾病对于降低死亡率至关重要.
计算机断层扫描 (CT) 扫描对于可视化与COVID-19相关的肺部异常至关重要.

研究的目的:

开发一种自动机器学习 (ML) 和深度学习 (DL) 方法,使用CT扫描图像检测COVID-19.
提高诊断准确度,促进肺部疾病的早期识别.
评估拟议方法的性能与已建立的预训练模型相比.

主要方法:

使用了11407张CT扫描图像 (7397张COVID-19,4010张正常) 的数据集.
开发了一种基于区域的无监督聚类技术,用于图像细分.
采用轮变换和卷积神经网络 (CNN) 来进行特征提取和融合,使用二进制差异演化 (BDE) 进行优化.
实施了基于ML/DL的组合分类器,用于最终检测,使用五倍和泛化交叉验证进行验证.

主要成果:

拟议的自动化方法在CT图像中检测COVID-19时实现了99.98%的最先进的准确性.
整体分类器整合了融合特征,与几个预训练模型 (AlexNet,ResNet50,GoogleNet,VGG16,VGG19) 相比,表现优越.
修改后的基于区域的聚类和特征融合技术在捕获相关诊断信息方面是有效的.

结论:

开发的ML/DL自动化系统通过CT扫描为COVID-19检测提供了一个高度准确和高效的工具.
这种方法具有早期诊断的巨大潜力,有助于改善患者的治疗结果和减少医疗保健负担.
通过先进技术和整体分类提取的特征的融合为医学图像分析提供了强大的方法.

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