An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning

Santana, Maíra Araújo de; Gomes, Juliana Carneiro; Barbosa, Valter Augusto de Freitas; Lima, Clarisse Lins de; Bandeira, Jonathan; Valença, Marcelo Moraes; Souza, Ricardo Emmanuel de; Masood, Aras Ismael; Santos, Wellington Pinheiro dos

doi:10.1007/978-3-030-79753-9_15

Cited by 4 publications

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References 54 publications

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“…The excerpt from [ 106 ] used a binary classification IKONOS-CT to differentiate COVID-19 patients from non-COVID-19 using CT images. The classifiers that were used were multilayer perceptron, SVM, random tree, random forest, and Bayesian networks.…”

Section: Covid-19 Prediction Using Deep Learningmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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Section: Covid-19 Prediction Using Deep Learningmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Nanotechnology-based theranostic and prophylactic approaches against SARS-CoV-2

Dhar,

Gupta,

Saini

et al. 2023

Immunol Res

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Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

et al. 2023

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Purpose In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system.

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An Intelligent Tool to Support Diagnosis of Covid-19 by Texture Analysis of Computerized Tomography X-ray Images and Machine Learning

Cited by 4 publications

References 54 publications

A Comprehensive Review of Machine Learning Used to Combat COVID-19

A Comprehensive Review of Machine Learning Used to Combat COVID-19

Nanotechnology-based theranostic and prophylactic approaches against SARS-CoV-2

Rapid protocols to support COVID-19 clinical diagnosis based on hematological parameters

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