背景:肺部疾病,传染性和非传染性,是世界上最普遍的死亡原因。医学研究发现了肺炎,肺癌,和2019年冠状病毒病(COVID-19)作为突出的肺部疾病优先于其他疾病。成像模式,包括X光片,计算机断层扫描(CT)扫描,磁共振成像(MRI),正电子发射断层扫描(PET)扫描,和其他人,主要用于医学评估,因为它们提供可用作计算机辅助诊断系统的输入数据集的计算数据。成像数据集用于开发和评估机器学习(ML)方法,以分析和预测突出的肺部疾病。
目的:这篇综述分析了机器学习范式,成像模式\'利用率,以及突出肺部疾病的最新发展。此外,该研究还探索了各种公开可用的数据集,这些数据集被用于突出的肺部疾病。
方法:经过同行评审的著名学术研究数据库,即ScienceDirect,arXiv,IEEEXplore,MDPI,还有更多,用于搜索相关文章。应用关键字和组合用于搜索程序,主要考虑审查,比如肺炎,肺癌,COVID-19,各种成像模式,ML,卷积神经网络(CNN),迁移学习,和合奏学习。
结果:这项研究发现表明,X射线数据集是检测肺炎的首选,而CT扫描数据集主要用于检测肺癌。此外,在COVID-19检测中,X射线数据集优先于CT扫描数据集。分析显示,X射线和CT扫描已经超过了所有其他成像技术。已经观察到,使用CNN在识别突出的肺部疾病中产生高度的准确性和实用性。迁移学习和集成学习是CNN的补充技术,可以促进分析。此外,准确性是最受欢迎的评估指标。
BACKGROUND: Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases.
OBJECTIVE: This
review analyzes ML paradigms, imaging modalities\' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases.
METHODS: The well-known databases of academic studies that have been subjected to peer
review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for
review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning.
RESULTS: This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.