Applications of machine learning algorithms to support COVID-19 diagnosis using X-rays data information

Medeiros, Elias P.; Machado, Marcos R.; de Freitas, Emannuel Diego G.; da Silva, Daniel S.; de Souza, Renato William R.

doi:10.1016/j.eswa.2023.122029

Cited by 8 publications

(2 citation statements)

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“…ML, a subfield of artificial intelligence (AI), tries to make computers learn from data [ 11 ]. Consequently, ML offers an automated framework that may be utilized to detect or anticipate lung illnesses in their earliest stages compared to manual methods [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

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A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

Kumar,

Kumar

et al. 2024

BMC Med Imaging

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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.

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Section: Introductionmentioning

confidence: 99%

“… Using ML approaches may improve model interpretability and decision-making. ML systems in lung disease diagnosis benefit from regular validation based on real-world clinical results [ 10 – 12 ]. This review analyzes ML approaches for diagnosing lung diseases.…”

Section: Introductionmentioning

confidence: 99%