Medical image analysis using deep learning algorithms

Frontiers in Public Health, Journal Year: 2023, Volume and Issue: 11

Published: Nov. 7, 2023

In the field of medical image analysis within deep learning (DL), importance employing advanced DL techniques cannot be overstated. has achieved impressive results in various areas, making it particularly noteworthy for healthcare. The integration with enables real-time vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes operational efficiency industry. This extensive review existing literature conducts a thorough examination most recent (DL) approaches designed to address difficulties faced healthcare, focusing on use algorithms analysis. Falling all investigated papers into five different categories terms their techniques, we have assessed them according some critical parameters. Through systematic categorization state-of-the-art such as Convolutional Neural Networks (CNNs), Recurrent (RNNs), Generative Adversarial (GANs), Long Short-term Memory (LSTM) models, hybrid this study explores underlying principles, advantages, limitations, methodologies, simulation environments, datasets. Based our results, Python was frequent programming language used implementing proposed methods papers. Notably, majority scrutinized were published 2021, underscoring contemporaneous nature research. Moreover, accentuates forefront advancements practical applications realm analysis, while simultaneously addressing challenges hinder widespread implementation domains. These discerned serve compelling impetuses future studies aimed at progressive advancement evaluation metrics employed across reviewed articles encompass broad spectrum features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, generalizability.

Language: Английский

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The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(11), P. 5757 - 5797

Published: Jan. 13, 2024

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance ML medical and bioinformatics owing to its accuracy, investigators discussed multiple solutions for developing function challenges using deep (DL) techniques. The importance DL Internet Things (IoT)-based bio- informatics lies ability analyze interpret large amounts complex diverse data real time, providing insights that can improve healthcare outcomes increase efficiency industry. Several applications IoT-based include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, drug discovery. review aims comprehensively evaluate synthesize existing body literature on applying intersection IoT with informatics. In this paper, we categorized most cutting-edge issues into five categories based technique utilized: convolutional neural network , recurrent generative adversarial multilayer perception hybrid methods. A systematic was applied study each one terms effective properties, like main idea, benefits, drawbacks, methods, simulation environment, datasets. After that, research approaches concerns emphasized. addition, several contributed implementation have been addressed, which are predicted motivate more studies develop progressively. According findings, articles evaluated features sensitivity, specificity, F -score, latency, adaptability, scalability.

Language: Английский

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Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 149, P. 102779 - 102779

Published: Jan. 24, 2024

Language: Английский

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The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Sustainability, Journal Year: 2023, Volume and Issue: 15(16), P. 12406 - 12406

Published: Aug. 15, 2023

With the swift pace of development artificial intelligence (AI) in diverse spheres, medical and healthcare fields are utilizing machine learning (ML) methodologies numerous inventive ways. ML techniques have outstripped formerly state-of-the-art practices, yielding faster more precise outcomes. Healthcare practitioners increasingly drawn to this technology their initiatives relating Internet Behavior (IoB). This area research scrutinizes rationales, approaches, timing human adoption, encompassing domains Things (IoT), behavioral science, edge analytics. The significance applications based on IoB stems from its ability analyze interpret copious amounts complex data instantly, providing innovative perspectives that can enhance outcomes boost efficiency IoB-based procedures thus aid diagnoses, treatment protocols, clinical decision making. As a result inadequacy thorough inquiry into employment ML-based approaches context using for applications, we conducted study subject matter, introducing novel taxonomy underscores need employ each method distinctively. objective mind, classified cutting-edge solutions challenges five categories, which convolutional neural networks (CNNs), recurrent (RNNs), deep (DNNs), multilayer perceptions (MLPs), hybrid methods. In order delve deeper, systematic literature review (SLR) examined critical factors, such as primary concept, benefits, drawbacks, simulation environment, datasets. Subsequently, highlighted pioneering studies issues. Moreover, several related implementation medicine been tackled, thereby gradually fostering further endeavors health studies. Our findings indicated Tensorflow was most commonly utilized setting, accounting 24% proposed by researchers. Additionally, accuracy deemed be crucial parameter majority papers.

Language: Английский

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The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Transactions on Emerging Telecommunications Technologies, Journal Year: 2024, Volume and Issue: 35(6)

Published: May 21, 2024

Abstract Nature‐inspired algorithms revolve around the intersection of nature‐inspired and IoT within healthcare domain. This domain addresses emerging trends potential synergies between computational approaches technologies for advancing services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, efficacy IoT‐based healthcare. We provide insights into practical aspects limitations such applications through a systematic literature review. Specifically, we address need comprehensive understanding healthcare, identifying as lack standardized evaluation metrics studies on challenges security considerations. By bridging these gaps, our paper offers directions future this domain, exploring diverse landscape chosen methodology is Systematic Literature Review (SLR) investigate related papers rigorously. Categorizing groups genetic algorithms, particle swarm optimization, cuckoo ant colony other approaches, hybrid methods, employ meticulous classification based critical criteria. MATLAB emerges predominant programming language, constituting 37.9% cases, showcasing prevalent choice among researchers. emphasizes adaptability paramount parameter, accounting 18.4% shedding light attributes, limitations, development, review contribute dynamic

Language: Английский

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Trends in using deep learning algorithms in biomedical prediction systems

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Nov. 9, 2023

In the domain of using DL-based methods in medical and healthcare prediction systems, utilization state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy this context. The integration with health systems enables real-time analysis vast intricate datasets, yielding insights that significantly enhance outcomes operational efficiency industry. This comprehensive literature review systematically investigates latest solutions for challenges encountered healthcare, a specific emphasis on applications domain. By categorizing cutting-edge approaches into distinct categories, including convolutional neural networks (CNNs), recurrent (RNNs), generative adversarial (GANs), long short-term memory (LSTM) models, support vector machine (SVM), hybrid study delves their underlying principles, merits, limitations, methodologies, simulation environments, datasets. Notably, majority scrutinized articles were published 2022, underscoring contemporaneous nature research. Moreover, accentuates forefront advancements techniques practical within realm while simultaneously addressing hinder widespread implementation image segmentation domains. These discerned serve as compelling impetuses future studies aimed at progressive advancement systems. evaluation metrics employed reviewed encompass broad spectrum features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, scalability.

Language: Английский

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Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108435 - 108435

Published: April 25, 2024

Language: Английский

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Deep study on autonomous learning techniques for complex pattern recognition in interconnected information systems

Computer Science Review, Journal Year: 2024, Volume and Issue: 54, P. 100666 - 100666

Published: Sept. 20, 2024

Language: Английский

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Applying self-supervised learning to network intrusion detection for network flows with graph neural network

Computer Networks, Journal Year: 2024, Volume and Issue: 248, P. 110495 - 110495

Published: May 10, 2024

Language: Английский

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Enhancing Cybersecurity through AI and ML: Strategies, Challenges, and Future Directions

Journal of Information Security, Journal Year: 2024, Volume and Issue: 15(03), P. 320 - 339

Published: Jan. 1, 2024

The landscape of cybersecurity is rapidly evolving due to the advancement and integration Artificial Intelligence (AI) Machine Learning (ML). This paper explores crucial role AI ML in enhancing defenses against increasingly sophisticated cyber threats, while also highlighting new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, predictive modeling, dual-edged nature examined. Significant challenges such as data privacy, continuous training models, manipulation risks, ethical concerns are addressed. emphasizes balanced approach leverages innovation alongside rigorous standards robust practices. facilitates collaboration among various stakeholders develop guidelines ensure responsible effective use cybersecurity, aiming enhance system integrity privacy without compromising security.

Language: Английский

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