Hybridization of particle swarm optimization algorithm with neural network for COVID‐19 using computerized tomography scan and clinical parameters DOI Creative Commons

Humam Adnan Sameer,

Sadik Kamel Gharghan, Ammar Hussein Mutlag

et al.

The Journal of Engineering, Journal Year: 2023, Volume and Issue: 2023(2)

Published: Jan. 23, 2023

The 2019 coronavirus disease began in Wuhan, China, and spread worldwide. This pandemic was concerning, given its significant worrying impact on human health. Strategies to manage the begin with diagnosing infection, often using real-time reverse transcription polymerase chain reaction (RT-PCR) assay. However, this process is time intensive. Therefore, alternative rapid methods diagnose high accuracy are needed. X-ray computerized tomography (CT) scans reasonable solutions for diagnosis. dataset of 500 patients tested, including 286 uninfected 214 infected COVID-19. Clinical parameters, heart rate (HR), temperature (T), blood oxygen level, D-dimer, CT scan, red-green-blue (RGB) pixel values left right lungs, were collected from used train an artificial neural network (ANN) coronavirus. ANN hybridized a particle swarm optimization (PSO) algorithm improve diagnosis accuracy. results show that proposed PSO-ANN method significantly improved (98.93%), sensitivity (100%), specificity (98.13%). effectiveness confirmed by comparing findings those previous studies.

Language: Английский

Optimized Deep Learning Model using Medical Imaging for CAD Applications DOI Open Access

Deepanshi Deepanshi,

Sameer Malik, Madhuri Gupta

et al.

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 3910 - 3921

Published: Jan. 1, 2025

Language: Английский

Citations

0
Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection DOI Creative Commons

Suya Jin,

Guiyan Liu, Qifeng Bai

et al.

Mathematics, Journal Year: 2023, Volume and Issue: 11(6), P. 1279 - 1279

Published: March 7, 2023

Deep learning is a sub-discipline of artificial intelligence that uses neural networks, machine technique, to extract patterns and make predictions from large datasets. In recent years, it has achieved rapid development widely used in numerous disciplines with fruitful results. Learning valuable information complex, high-dimensional, heterogeneous biomedical data key challenge transforming healthcare. this review, we provide an overview emerging deep-learning techniques, COVID-19 research involving deep learning, concrete examples methods diagnosis, prognosis, treatment management. can process medical imaging data, laboratory test results, other relevant diagnose diseases judge disease progression even recommend plans drug-use strategies accelerate drug improve quality. Furthermore, help governments develop proper prevention control measures. We also assess the current limitations challenges therapy precision for COVID-19, including lack phenotypically abundant need more interpretable models. Finally, discuss how barriers be overcome enable future clinical applications learning.

Language: Английский

Citations

9
A tree-based explainable AI model for early detection of Covid-19 using physiological data DOI Creative Commons

Manar Abu Talib,

Yaman Afadar,

Qassim Nasir

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: June 24, 2024

Abstract With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) Data Science techniques for disease detection. Although cases declined, there are still deaths around world. Therefore, early detection before onset symptoms has become crucial reducing its extensive impact. Fortunately, wearable devices such as smartwatches proven to be valuable sources physiological data, including Heart Rate (HR) sleep quality, enabling inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts heart rate data predict probability infection symptoms. We train three main model architectures: Gradient Boosting classifier (GB), CatBoost trees, TabNet analyze compare their respective performances. also add interpretability layer our best-performing model, which clarifies prediction results allows a detailed assessment effectiveness. Moreover, created private by gathering from Fitbit guarantee reliability avoid bias. The identical set models was then applied using same pre-trained models, were documented. Using tree-based method, outperformed previous with accuracy 85% on publicly available dataset. Furthermore, produced 81% when You will find source code link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .

Language: Английский

Citations

3
Comprehensive Survey of Machine Learning Systems for COVID-19 Detection DOI Creative Commons
Bayan Al-Saaidah,

Moh’d Rasoul Al-Hadidi,

Heba Al-Nsour

et al.

Journal of Imaging, Journal Year: 2022, Volume and Issue: 8(10), P. 267 - 267

Published: Sept. 30, 2022

The last two years are considered the most crucial and critical period of COVID-19 pandemic affecting life aspects worldwide. This virus spreads quickly within a short period, increasing fatality rate associated with virus. From clinical perspective, several diagnosis methods carried out for early detection to avoid propagation. However, capabilities these limited have various challenges. Consequently, many studies been performed automated without involving manual intervention allowing an accurate fast decision. As is case other diseases medical issues, Artificial Intelligence (AI) provides community potential technical solutions that help doctors radiologists diagnose based on chest images. In this paper, comprehensive review mentioned AI-based solution proposals conducted. More than 200 papers reviewed analyzed, 145 articles extensively examined specify proposed AI mechanisms A examination advantages shortcomings illustrated summarized. Several findings concluded as result deep analysis all previous works using machine learning detection, segmentation, classification.

Language: Английский

Citations

14
Role of XAI in building a super smart society 5.0 DOI

M. Kiruthika,

K. Moorthi,

M. Anousouya Devi

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 295 - 326

Published: Jan. 1, 2024

Language: Английский

Citations

2
Identification of Unique Genomic Signatures in Viral Immunogenic Syndrome (VIS) Using FIMAR and FCSM Methods for Development of Effective Diagnostic and Therapeutic Strategies DOI Open Access

DUBEY Shivendra,

VERMA Dinesh Kumar,

Karthik Mahesh

et al.

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Journal Year: 2024, Volume and Issue: 58(2/2024), P. 165 - 181

Published: June 12, 2024

The "Viral Immunogenic Syndrome" (VIS) incorporates the concepts of "viral" and "immunogenic" to emphasise pathogenic character illness immunological response it generates, as well word "syndrome" describe broad set symptoms consequences.Our research focused on analyzing COVID-19 genome sequence using a proposed framework improve computation time model efficiency.We also aimed identify frequent patterns, missing indices, variations in while comparing performance with varying minimum support existing models.We used FCSM classify genomic sequences detect calculating time.Additionally, we novel utilizing FIMAR nucleotide compute consecutive sets, resulting more efficient accurate approach than methods.Our study shows that algorithms is 94.34% system for computing sequence.We identified 0.2% 1.61% variation USA China datasets, respectively, which failed detect.Additionally, conducted comparative an Apriori methods patterns.In this work, present analysis substitution rate at each isolation step.

Language: Английский

Citations

2
Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing DOI Creative Commons

Shivendra Dubey,

Dinesh Verma, Mahesh Kumar

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2062 - e2062

Published: July 30, 2024

The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated a pandemic by the World Health Organization due its highly infectious nature and associated public health risks it poses globally. Identifying critical factors for predicting mortality is essential improving patient therapy. Unlike other data types, such computed tomography scans, x-radiation, ultrasounds, basic blood test results are widely accessible can aid in mortality. present research advocates utilization of machine learning (ML) methodologies likelihood disease like COVID-19 leveraging data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, hs-CRP (high-sensitivity C-reactive protein) five extremely potent characteristics that, when combined, accurately predict 96% cases. By combining XGBoost feature importance with neural network classification, optimal approach exceptional accuracy from disease, along achieving precision rate 90% up 16 days before event. studies suggested model’s excellent predictive performance practicality were confirmed through testing three instances that depended on outcome. carefully analyzing identifying patterns these significant biomarkers insightful information has obtained simple application. This study offers potential remedies could accelerate decision-making targeted medical treatments within healthcare systems, utilizing timely, accurate, reliable method.

Language: Английский

Citations

2
Explaining COVID-19 diagnosis with Taylor decompositions DOI Open Access
Mohammad Mehedi Hassan, Salman A. AlQahtani, Abdulhameed Alelaiwi

et al.

Neural Computing and Applications, Journal Year: 2022, Volume and Issue: 35(30), P. 22087 - 22100

Published: Nov. 17, 2022

Language: Английский

Citations

9
An explainable AI approach for diagnosis of COVID-19 using MALDI-ToF mass spectrometry DOI
Venkata Devesh Reddy Seethi, Zane LaCasse, Prajkta Chivte

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 236, P. 121226 - 121226

Published: Aug. 19, 2023

Language: Английский

Citations

5
Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans DOI Creative Commons
Pedro Crosara Motta, Paulo César Cortez, Bruno Riccelli dos Santos Silva

et al.

Bioengineering, Journal Year: 2023, Volume and Issue: 10(5), P. 529 - 529

Published: April 26, 2023

Even with over 80% of the population being vaccinated against COVID-19, disease continues to claim victims. Therefore, it is crucial have a secure Computer-Aided Diagnostic system that can assist in identifying COVID-19 and determining necessary level care. This especially important Intensive Care Unit monitor progression or regression fight this epidemic. To accomplish this, we merged public datasets from literature train lung lesion segmentation models five different distributions. We then trained eight CNN for Common-Acquired Pneumonia classification. If examination was classified as quantified lesions assessed severity full CT scan. validate system, used Resnetxt101 Unet++ Mobilenet Unet segmentation, respectively, achieving accuracy 98.05%, F1-score 98.70%, precision 98.7%, recall specificity 96.05%. accomplished just 19.70 s per scan, external validation on SPGC dataset. Finally, when classifying these detected lesions, Densenet201 achieved 90.47%, 93.85%, 88.42%, 100.0%, 65.07%. The results demonstrate our pipeline correctly detect segment due scans. It differentiate two classes normal exams, indicating efficient effective assessing condition.

Language: Английский

Citations

5