An effective detection of COVID‐19 using adaptive dual‐stage horse herd bidirectional <scp>long short‐term memory</scp> framework

Mannepalli, Durga Prasad; Namdeo, Varsha

doi:10.1002/ima.22747

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…Kumar et al 22 propose two models: (i) designing DNN based on the fractal feature, and (ii) designing CNN via lung x‐ray images. Mannepalli et al 23 propose an effectual detection of COVID‐19 by an adaptive dual‐stage horse herd bidirectional long short‐term memory framework. Kanwal et al 24 propose COVID‐opt‐aiNet, which is a clinical decision support system (DSS).…”

Section: X‐ray Radiographmentioning

confidence: 99%

Fighting against COVID‐19: Innovations and applications

Zhang

2023

Int J Imaging Syst Tech

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Section: X‐ray Radiographmentioning

confidence: 99%

Fighting against COVID‐19: Innovations and applications

Zhang

2023

Int J Imaging Syst Tech

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“…Research in [ 92 ], utilizes preprocessing techniques and a bidirectional LSTM to classify images as normal, viral pneumonia, lung opacity, or COVID-19. The image dataset used contains 3616 COVID-19 images, 10,192 normal images, 6012 Lung opacity images, and 1345 viral pneumonia images.…”

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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A rapid literature review on ensemble algorithms for COVID-19 classification using image-based exams

Portela,

Cortes,

da Silva

2023

HIS

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The world recently has faced the COVID-19 pandemic, a disease caused by the severe acute respiratory syndrome. The main features of this disease are the rapid spread and high-level mortality. The illness led to the rapid development of a vaccine that we know can fight against the virus; however, we do not know the actual vaccine’s effectiveness. Thus, the early detection of the disease is still necessary to provide a suitable course of action. To help with early detection, intelligent methods such as machine learning and computational intelligence associated with computer vision algorithms can be used in a fast and efficient classification process, especially using ensemble methods that present similar efficiency to traditional machine learning algorithms in the worst-case scenario. In this context, this review aims to answer four questions: (i) the most used ensemble technique, (ii) the accuracy those methods reached, (iii) the classes involved in the classification task, (iv) the main machine learning algorithms and models, and (v) the dataset used in the experiments.

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An effective detection of COVID‐19 using adaptive dual‐stage horse herd bidirectional long short‐term memory framework

Cited by 3 publications

References 51 publications

Fighting against COVID‐19: Innovations and applications

Fighting against COVID‐19: Innovations and applications

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

A rapid literature review on ensemble algorithms for COVID-19 classification using image-based exams

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