A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

Alexandria Engineering Journal, Journal Year: 2022, Volume and Issue: 64, P. 923 - 935

Published: Nov. 2, 2022

In 2019, the world experienced rapid outbreak of Covid-19 pandemic creating an alarming situation worldwide. The virus targets respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed pneumonia, lung cancer, or TB. Thus, early diagnosis COVID-19 is critical since disease provoke patients' mortality. Chest X-ray (CXR) commonly employed in healthcare sector where both quick precise supplied. Deep learning algorithms have proved extraordinary capabilities terms diseases detection classification. They facilitate expedite process save time for medical practitioners. this paper, a deep (DL) architecture multi-class classification Pneumonia, Lung Cancer, tuberculosis (TB), Opacity, most recently proposed. Tremendous CXR images 3615 COVID-19, 6012 opacity, 5870 20,000 1400 tuberculosis, 10,192 normal were resized, normalized, randomly split to fit DL requirements. classification, we utilized pre-trained model, VGG19 followed by three blocks convolutional neural network (CNN) feature extraction fully connected at stage. experimental results revealed that our proposed + CNN outperformed existing work 96.48 % accuracy, 93.75 recall, 97.56 precision, 95.62 F1 score, 99.82 area under curve (AUC). model delivered superior performance allowing practitioners diagnose treat patients more quickly efficiently.

Language: Английский

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A unique Markov chain Monte Carlo method for forecasting wind power utilizing time series model

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 74, P. 51 - 63

Published: May 17, 2023

Concerns that impair human societies frequently include a heavy dependence on petroleum and coal emissions of greenhouse gases. Thus, adopting renewable energy sources, such as wind power, has become practical solution to this problem. Therefore, carry out the research velocity energy, time-series structure is necessary. This study uses Markov chain Monte Carlo approach Seasonal Autoregressive Integrated Moving Average (SARIMA) model estimate short-term long-term sustained winds. The significance building system initially discussed, after which framework based SARIMA presented, followed by Long-term speed projection. Furthermore, methodology utilizing method (MCMC) suggested establish analysis. draws for data maintain stochasticity realize probability transition matrix. Gibbs sampling employed well. model's forecasting abilities were tested using original database various efficiency assessment measures, including Root Mean Square Error (RMSE) Absolute Percentage (MAPE) with 13.09 1.03. In study, highest KGE WI well lowest RMSE MAE was chosen. findings demonstrate used in operation provides outstanding predictability.

Language: Английский

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Identification of olive leaf disease through optimized deep learning approach

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 72, P. 213 - 224

Published: April 11, 2023

Language: Английский

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The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals

Oeconomia Copernicana, Journal Year: 2024, Volume and Issue: 15(1), P. 27 - 58

Published: March 30, 2024

Research background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis symptom tracing, optimize intensive care unit admission, use clinical data to determine patient prioritization mortality risk, being pivotal qualitative provision, reducing errors, increasing survival rates, thus diminishing the massive healthcare system burden relation severe inpatient stay duration, while operational costs throughout organizational management of hospitals. Data-driven financial scenario-based contingency planning, predictive modelling tools, risk pooling mechanisms should be deployed for additional equipment unforeseen demand expenses. Purpose article: We show that deep decision making systems likelihood treatment outcomes with regard susceptible, infected, recovered individuals, performing accurate analyses by modeling based on vital signs, surveillance data, infection-related biomarkers, furthering hospital facility optimization terms bed allocation. Methods: The review software employed article screening quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, SRDR. Findings & value added: support tools forecast spread, confirmed cases, infection rates data-driven appropriate resource allocations effective therapeutic protocol development, determining suitable measures regulations using symptoms comorbidities, laboratory records across units, impacting financing infrastructure. As a result heightened personal protective equipment, pharmacy medication, outpatient treatment, supplies, revenue loss vulnerability occur, also due expenses related hiring staff critical expenditures. Hospital care, screening, capacity expansion, lead further losses affecting frontline workers patients.

Language: Английский

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Enhanced framework for COVID-19 prediction with computed tomography scan images using dense convolutional neural network and novel loss function

Computers & Electrical Engineering, Journal Year: 2022, Volume and Issue: 105, P. 108479 - 108479

Published: Nov. 14, 2022

Language: Английский

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Automated classification of giant virus genomes using a random forest model built on trademark protein families

npj Viruses, Journal Year: 2024, Volume and Issue: 2(1)

Published: March 8, 2024

Abstract Viruses of the phylum Nucleocytoviricota , often referred to as “giant viruses,” are prevalent in various environments around globe and play significant roles shaping eukaryotic diversity activities global ecosystems. Given extensive phylogenetic within this viral group highly complex composition their genomes, taxonomic classification giant viruses, particularly incomplete metagenome-assembled genomes (MAGs) can present a considerable challenge. Here we developed TIGTOG ( T axonomic I nformation G iant viruses using rademark O rthologous roups), machine learning-based approach predict novel virus MAGs based on profiles protein family content. We applied random forest algorithm training set 1531 quality-checked, phylogenetically diverse pre-selected sets orthologous groups (GVOGs). The models were predictive assignments with cross-validation accuracy 99.6% at order level 97.3% level. found that no individual GVOGs or genome features significantly influenced algorithm’s performance models’ predictions, indicating predictions comprehensive genomic signature, which reduced necessity fixed marker genes for assigning purposes. Our validated an independent test 823 varied completeness taxonomy demonstrated 98.6% 95.9% level, respectively. results indicate be used accurately classify large DNA different levels provide fast accurate method viruses. This could easily adapted other groups.

Language: Английский

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FCMCPS-COVID: AI propelled fog–cloud inspired scalable medical cyber-physical system, specific to coronavirus disease

Internet of Things, Journal Year: 2023, Volume and Issue: 23, P. 100828 - 100828

Published: May 26, 2023

Language: Английский

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Advances in artificial intelligence for accurate and timely diagnosis of COVID-19: A comprehensive review of medical imaging analysis

Scientific African, Journal Year: 2023, Volume and Issue: 22, P. e01961 - e01961

Published: Nov. 1, 2023

In December 2019, the first case of coronavirus 2019 (COVID-19) appeared in China, quickly leading to a global pandemic. Early and accurate diagnosis is crucial for effective disease management. While reverse transcription polymerase chain reaction (RT-PCR) standard diagnostic test, it may yield false negative misleading results. Artificial intelligence (AI) systems are accelerating transformation medical field, particularly early detection diagnosis. Recent research has combined AI with imaging modalities, such as chest X-ray (CXR) computed tomography (CT), detect virus, aiding doctors making decisions reducing misdiagnosis rates. this article, we conducted systematic review high-quality articles published high-impact journals that examined convolutional neural network (CNN)-based methods detecting COVID-19 from radiographic or CT images discussed associated issues. We synthesized publicly available datasets evaluation measures, including accuracy, sensitivity, specificity, F1 score, each system used automatic using several well-performing CNN architectures. Furthermore, identified key questions future directions field. Our results show use considerable potential improve accuracy reduce Nevertheless, important challenges must be addressed, limited access need rigorous model validation. Additionally, generalization models different populations contexts needs examined. findings underscore directions, exploration deep learning smaller datasets, enhancing performance complex cases, designing practical deployment clinical settings.

Language: Английский

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Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 86, P. 105026 - 105026

Published: May 15, 2023

Language: Английский

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Recent Progress in ZnO-Based Nanostructures for Photocatalytic Antimicrobial in Water Treatment: A Review

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(15), P. 7910 - 7910

Published: Aug. 7, 2022

Advances in nanotechnology have led to the development of antimicrobial technology nanomaterials. In recent years, photocatalytic antibacterial disinfection methods with ZnO-based nanomaterials attracted extensive attention scientific community. addition, recently widely and speedily spread viral microorganisms, such as COVID-19 monkeypox virus, aroused global concerns. Traditional water purification are inhibited due increased resistance bacteria viruses. Exploring new effective materials has important practical application value. This review is a comprehensive overview progress following: (i) preparation comparison between methods; (ii) types for antibacterials treatment; (iii) studying activities (iv) mechanisms antibacterials. Subsequently, use different doping strategies enhance properties also emphatically discussed. Finally, future research applications activity proposed.

Language: Английский

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