A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images

Musha, Ahmmad; Mamun, Abdullah Al; Tahabilder, Anik; Hossen, J.; Jahan, Busrat; Ranjbari, Sima

doi:10.11591/ijece.v12i4.pp3655-3664

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…In total, 97.5% accuracy was achieved using the preprocessing techniques and C-COVIDNet, which beats other state-of-the-art CNNs. The study in [ 66 ] proposes a novel model to detect COVID-19 and pneumonia using CXR images in real-time. The dataset used in the study contains 1102 COVID-19, 1341 normal, 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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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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“…However, the CNN model does not encode the object's position and orientation and needs a lot of training data to work efficiently. These networks can be trained to recognize visual patterns, including voice, handwriting, and image recognition [27], [35]- [40]. As a result, modern NNs are significantly more potent than their predecessors.…”

Section: Neural Networkmentioning

confidence: 99%

Machine learning approaches in the diagnosis of infectious diseases: a review

et al. 2022

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Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.

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“…erefore, they need comprehensive decision support models that track and measure the financial impact of their production and distribution decision by integrating various financial performances. Moreover, this integration makes a "common language" between supply chain managers and financial managers and improves cooperation between them [42,43]. is study suggests a mathematical programming decision model that considers the physical and financial aspects of a supply chain planning problem simultaneously.…”

Section: Managerial Insightmentioning

confidence: 99%

A Mathematical Programming Approach to Supply Chain Network Design considering Shareholder Value Creation

Biglar

Hamta

Rad

2022

Discrete Dynamics in Nature and Society

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One of the main goals of supply chain management is to ensure proper flows of products and information through all nodes to supply them in the right place at the right time. To achieve this objective, it is very important to consider flows of products and finances among supply chain nodes. Traditionally, operational and financial processes have been optimized as separate problems. The developed model addresses the problem of designing a supply chain network and tries to integrate both areas of operations and financial aspects to maximize the value created and measured by the Shareholder Value Analysis (SVA). The results show that with appropriate financial decisions, creating more value for the company and its shareholders is achievable. The developed model with a new financial approach is able to improve the total created shareholder value as much as 0.7% larger than the SVA obtained without financial aspects and 0.93% larger than the value created by the basic model. The main reason for an increase in value creation is due to new operational and financial aspects, which mainly show the possibility of closing facilities and bank debt repayments. To validate and show the applicability of the proposed model, it was solved by GAMS-BARON solver with data provided from the literature. Sensitivity analyses on financial parameters were performed to evaluate the results.

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A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images

Cited by 11 publications

References 18 publications

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

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

Machine learning approaches in the diagnosis of infectious diseases: a review

A Mathematical Programming Approach to Supply Chain Network Design considering Shareholder Value Creation

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