Label-free electrochemical immunosensor employing new redox probes/porous organic polymers/graphene oxide nanocomposite towards multiplex detection of three SARS-COV2-induced storming proteins for severe COVID-19 diagnosis

Materials Today Chemistry, Journal Year: 2024, Volume and Issue: 35, P. 101906 - 101906

Published: Jan. 1, 2024

Language: Английский

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Deep Learning and Federated Learning for Screening COVID-19: A Review

BioMedInformatics, Journal Year: 2023, Volume and Issue: 3(3), P. 691 - 713

Published: Sept. 1, 2023

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts thorough study the use deep learning (DL) and federated (FL) approaches to COVID-19 screening. To begin, an evaluation research articles published between 1 January 2020 28 June 2023 is presented, considering preferred reporting items systematic reviews meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, ultrasound images, in terms number samples, classes datasets. Following that, description existing DL algorithms applied offered. Additionally, summary recent work FL for screening provided. Efforts improve quality models are comprehensively reviewed objectively evaluated.

Language: Английский

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Small size CNN-Based COVID-19 Disease Prediction System using CT scan images on PaaS cloud

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(21), P. 60655 - 60687

Published: Jan. 3, 2024

Language: Английский

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DICA-Net: optimizing chest X-ray classification with attention U-Net and pigeon local search

Network Modeling Analysis in Health Informatics and Bioinformatics, Journal Year: 2025, Volume and Issue: 14(1)

Published: April 9, 2025

Language: Английский

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Rapid and accurate classification of Covid-19 severity in CT scans using DRIEN model and advanced feature selection

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 109, P. 108052 - 108052

Published: May 8, 2025

Language: Английский

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Cn2a-capsnet: a capsule network and CNN-attention based method for COVID-19 chest X-ray image diagnosis

Deleted Journal, Journal Year: 2024, Volume and Issue: 6(4)

Published: April 4, 2024

Abstract Due to its high infectivity, COVID-19 has rapidly spread worldwide, emerging as one of the most severe and urgent diseases faced by global community in recent years. Currently, deep learning-based diagnostic methods can automatically detect cases from chest X-ray images. However, these often rely on large-scale labeled datasets. To address this limitation, we propose a novel neural network model called CN2A-CapsNet, aiming enhance automatic diagnosis images through efficient feature extraction techniques. Specifically, combine CNN with an attention mechanism form CN2A model, which efficiently mines relevant information Additionally, incorporate capsule networks leverage their ability understand spatial information, ultimately achieving extraction. Through validation publicly available image dataset, our achieved 98.54% accuracy 99.01% recall rate binary classification task (COVID-19/Normal) six-fold cross-validation dataset. In three-class (COVID-19/Pneumonia/Normal), it attained 96.71% 98.34% rate. Compared previous state-of-the-art models, CN2A-CapsNet exhibits notable advantages diagnosing cases, specifically even small-scale

Language: Английский

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Conditional cascaded network (CCN) approach for diagnosis of COVID-19 in chest X-ray and CT images using transfer learning

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 87, P. 105563 - 105563

Published: Oct. 3, 2023

Language: Английский

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Web Diagnosis for COVID-19 and Pneumonia Based on Computed Tomography Scans and X-rays

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 203 - 221

Published: Jan. 1, 2024

Language: Английский

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Comparative Analysis on Available Technique for the Detection of Covid-19 through CT-Scan and X-Ray using Machine Learning: A Systematic Review

Published: April 3, 2023

(1) Background: In the year of 2020 Covid-19 was declared epidemic by WHO. From that time millions people were affected and died this disease. The main detection process for is RT-PCR test or reverse polymerase transcription chain reaction test. One reason spreading disease so much lack efficiency in Sampling error low viral load two reasons what testing faced such problems. Lung infection a very common symptom covid-19 patients, so, CT scan chest X-ray imaging technique can be applied to detect patient at early stage infection. Which will effective also better option test; (2) Methods: We searched data Scopus articles published between 2023. initial set 189, from which 21 eventually selected exclusion criteria; (3) Results: A total thirteen (61.90%) found working on detecting extracting individually. Three (14.28%) those focused hybrid model Image Data. Another four made comparison Covid-19, pneumonia normal person identify patient. Where others have worked unsupervised learning methods SVM Covid-19.; (4) Conclusions: conducted systematic review studies been up time, with purpose present summary evidence about COVID-19. article, we summarized critically reviewed literatures development application both different AI ML images find solution covid-19.

Language: Английский

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Deep Hybrid Learning Approaches for COVID-19 Virus Detection Using Chest X-ray Images

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(7)

Published: Jan. 1, 2024

This paper introduces a novel deep learning framework for highly accurate COVID-19 detection using chest X-ray images. The proposed model tackles the challenge by combining stacked Convolutional Neural Network models superior feature extraction to potentially enhance interpretability. achieved high accuracy in distinguishing from healthy cases. study demonstrates potential of hybrid detection, paving way its application real-world settings. Future research directions could explore methods further refine model's capabilities. Overall, this work contributes significantly development robust deep-learning with broader use medical image analysis.

Language: Английский

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