A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107212 - 107212

Published: July 6, 2023

Language: Английский

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Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(8), P. 176 - 176

Published: July 23, 2024

This paper addresses the significant problem of identifying relevant background and contextual literature related to deep learning (DL) as an evolving technology in order provide a comprehensive analysis application DL specific pneumonia detection via chest X-ray (CXR) imaging, which is most common cost-effective imaging technique available worldwide for diagnosis. particular key period associated with COVID-19, 2020–2023, explain, analyze, systematically evaluate limitations approaches determine their relative levels effectiveness. The context applied both aid automated substitute existing expert radiography professionals, who often have limited availability, elaborated detail. rationale undertaken research provided, along justification resources adopted relevance. explanatory text subsequent analyses are intended sufficient detail being addressed, solutions, these, ranging from more general. Indeed, our evaluation agree generally held view that use transformers, specifically, vision transformers (ViTs), promising obtaining further effective results area using CXR images. However, ViTs require extensive address several limitations, specifically following: biased datasets, data code ease model can be explained, systematic methods accurate comparison, notion class imbalance possibility adversarial attacks, latter remains fundamental research.

Language: Английский

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The effectiveness of deep learning vs. traditional methods for lung disease diagnosis using chest X-ray images: A systematic review

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 147, P. 110817 - 110817

Published: Sept. 9, 2023

Language: Английский

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Utilizing Ensemble Learning for Detecting Multi-Modal Fake News

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 15037 - 15049

Published: Jan. 1, 2024

The spread of fake news has become a critical problem in recent years due extensive use social media platforms. False stories can go viral quickly, reaching millions people before they be mocked, i.e., false story claiming that celebrity died when he/she is still alive. Therefore, detecting essential for maintaining the integrity information and controlling misinformation, political polarization, ethics, security threats. From this perspective, we propose an ensemble learning-based detection multi-modal news. First, it exploits publicly available dataset Fakeddit consisting over 1 million samples Next, leverages Natural Language Processing (NLP) techniques preprocessing textual Then, gauges sentiment from text each After that, generates embeddings images corresponding by leveraging Visual Bidirectional Encoder Representations Transformers (V-BERT), respectively. Finally, passes to deep learning model training testing. 10-fold evaluation technique used check performance proposed approach. results are significant outperform state-of-the-art approaches with improvement 12.57%, 9.70%, 18.15%, 12.58%, 0.10, 3.07 accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Odds Ratio (OR),

Language: Английский

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A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(6), P. 4756 - 4780

Published: March 1, 2024

Abstract The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) rapid accurate identification diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing five-fold cross-validation method to ensure robustness our results, CNN model, optimized heterogeneous embedded devices, demonstrated superior performance. It achieved 98.56% accuracy, outperforming established networks like ResNet50, NASNetMobile, Xception, MobileNetV2, DenseNet121, ViT-B/16 across precision, recall, F1-score, AUC metrics. Notably, model requires significantly less computational power only 55 minutes average training time per fold, making it highly suitable resource-constrained environments. This study contributes developing efficient, in medical image analysis, underscoring their potential enhance point-of-care processes.

Language: Английский

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Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment

Biomedical Physics & Engineering Express, Journal Year: 2024, Volume and Issue: 10(4), P. 045005 - 045005

Published: April 25, 2024

Abstract The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early diagnostic systems. However, the technological limitations complexity disease make it challenging to implement an efficient screening system. AI-based CT image analysis techniques showing significant contributions development computer-assisted (CAD) systems screening. Various existing research groups working on implementing assessing classifying cancer. different structures inside is high comprehension information inherited by them more complex even after applying advanced feature extraction selection techniques. Traditional classical may struggle capture interdependencies between features. They get stuck local optima sometimes require additional exploration strategies. also with combinatorial optimization problems when applied prominent space. This paper proposed methodology overcome using Vision Transformer (FexViT) Feature Quantum Computing based Quadratic unconstrained binary (QC-FSelQUBO) technique. algorithm shows better performance compared other showed as evaluated necessary output measures, such accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, specificity, obtained 94.28%, 99.10%, 96.17%, 90.16% 97.46%. further advancement CAD essential meet demand reliable diagnosis cancer, which can be addressed leading quantum computation growing technology ahead.

Language: Английский

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An Adaptation of Hybrid Binary Optimization Algorithms for Medical Image Feature Selection in Neural Network for Classification of Breast Cancer

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 129018 - 129018

Published: Nov. 1, 2024

Language: Английский

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Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm

Lecture notes in electrical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 199 - 205

Published: Jan. 1, 2024

Language: Английский

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Optimizing Feature Selection for Industrial Casting Defect Detection Using QLESCA Optimizer

Lecture notes in electrical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 485 - 491

Published: Jan. 1, 2024

Language: Английский

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Attention Mechanism Used in Monocular Depth Estimation: An Overview

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(17), P. 9940 - 9940

Published: Sept. 2, 2023

Monocular depth estimation (MDE), as one of the fundamental tasks computer vision, plays important roles in downstream applications such virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based methods gained remarkable progress compared with traditional using visual cues. However, recent researches reveal that performance MDE CNN could be degraded due to local receptive field CNN. To bridge gap, various attention mechanisms were proposed model long-range dependency. Although reviews algorithms based on reported, a comprehensive outline how boosts is not explored yet. In this paper, we firstly categorize attention-related works into CNN-based, Transformer-based, hybrid (CNN–Transformer-based) approaches light mechanism impacts extraction global features. Secondly, discuss details contributions attention-based published from 2020 2022. Then, compare typical methods. Finally, challenges trends used are discussed.

Language: Английский

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