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

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

58
A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging DOI Creative Commons
Mélanie Champendal, Henning Müller, John O. Prior

et al.

European Journal of Radiology, Journal Year: 2023, Volume and Issue: 169, P. 111159 - 111159

Published: Oct. 21, 2023

PurposeTo review eXplainable Artificial Intelligence/(XAI) methods available for medical imaging/(MI).MethodA scoping was conducted following the Joanna Briggs Institute's methodology. The search performed on Pubmed, Embase, Cinhal, Web of Science, BioRxiv, MedRxiv, and Google Scholar. Studies published in French English after 2017 were included. Keyword combinations descriptors related to explainability, MI modalities employed. Two independent reviewers screened abstracts, titles full text, resolving differences through discussion.Results228 studies met criteria. XAI publications are increasing, targeting MRI (n=73), radiography (n=47), CT (n=46). Lung (n=82) brain (n=74) pathologies, Covid-19 (n=48), Alzheimer's disease (n=25), tumors (n=15) main pathologies explained. Explanations presented visually (n=186), numerically (n=67), rule-based (n=11), textually example-based (n=6). Commonly explained tasks include classification (n=89), prediction diagnosis (n=39), detection (n=29), segmentation (n=13), image quality improvement most frequently provided explanations local (78.1%), 5.7% global, 16.2% combined both global approaches. Post-hoc approaches predominantly used terminology varied, sometimes indistinctively using explainable (n=207), interpretable (n=187), understandable (n=112), transparent (n=61), reliable (n=31), intelligible (n=3).ConclusionThe number imaging is primarily focusing applying techniques MRI, CT, classifying predicting lung pathologies. Visual numerical output formats used. Terminology standardisation remains a challenge, as terms like "explainable" "interpretable" being indistinctively. Future development should consider user needs perspectives.

Language: Английский

Citations

22
Future of Drug Discovery: The Synergy of Edge Computing, Internet of Medical Things, and Deep Learning DOI Creative Commons
Mohammad Jamshidi, Omid Moztarzadeh, Alireza Jamshidi

et al.

Future Internet, Journal Year: 2023, Volume and Issue: 15(4), P. 142 - 142

Published: April 7, 2023

The global spread of COVID-19 highlights the urgency quickly finding drugs and vaccines suggests that similar challenges will arise in future. This underscores need for ongoing efforts to overcome obstacles involved development potential treatments. Although some progress has been made use Artificial Intelligence (AI) drug discovery, virologists, pharmaceutical companies, investors seek more long-term solutions greater investment emerging technologies. One solution aid drug-development process is combine capabilities Internet Medical Things (IoMT), edge computing (EC), deep learning (DL). Some practical frameworks techniques utilizing EC, IoMT, DL have proposed monitoring tracking infected individuals or high-risk areas. However, these technologies not widely utilized clinical trials. Given time-consuming nature traditional drug- vaccine-development methods, there a new AI-based platform can revolutionize industry. approach involves smartphones equipped with medical sensors collect transmit real-time physiological healthcare information on clinical-trial participants nearest nodes (EN). allows verification vast amount data large number short time frame, without restrictions latency, bandwidth, security constraints. collected be monitored by physicians researchers assess vaccine’s performance.

Language: Английский

Citations

20
The effectiveness of deep learning vs. traditional methods for lung disease diagnosis using chest X-ray images: A systematic review DOI

Samira Sajed,

Amir Sanati,

Jorge Esparteiro Garcia

et al.

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

Published: Sept. 9, 2023

Language: Английский

Citations

17
Explainable Artificial Intelligence (XAI) in healthcare: Interpretable Models for Clinical Decision Support DOI

Nitin Rane,

Saurabh Choudhary,

Jayesh Rane

et al.

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

In healthcare, the incorporation of Artificial Intelligence (AI) plays a pivotal role in enhancing diagnostic precision and guiding treatment decisions. Nevertheless, lack transparency conventional AI models poses challenges gaining trust clinicians comprehending rationale behind their This research paper explores Explainable (XAI) its application with specific focus on transparent designed for clinical decision support various medical disciplines. The initiates by underscoring crucial requirement interpretability systems within healthcare realm. Recognizing diverse nature specialties, study investigates tailored XAI approaches to meet distinctive needs areas such as radiology, pathology, cardiology, oncology. Through thorough review existing literature analysis, identifies key obstacles prospects implementing across varied contexts. field cornerstone imaging, proves beneficial elucidating decision-making procedures image analysis algorithms. probes into impact interpretable radiological diagnoses, examining how can seamlessly integrate AI-generated insights workflows. Within where is utmost importance, clarifies enhance histopathological assessments. By demystifying intricacies AI-driven pathology models, aims empower pathologists leverage these tools more accurate diagnoses. Cardiology, characterized complex interplay physiological parameters, benefits from offering intelligible explanations cardiovascular risk predictions recommendations. delves highlighting potential systems. Moreover, oncology, decisions hinge precise identification characterization tumors, aids unraveling intricate machine learning models. This, turn, fosters among oncologists utilizing personalized strategies.

Language: Английский

Citations

15
A fuzzy collaborative forecasting approach based on XAI applications for cycle time range estimation DOI
Toly Chen, Chi‐Wei Lin, Yu‐Cheng Lin

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 151, P. 111122 - 111122

Published: Dec. 6, 2023

Language: Английский

Citations

14
On the Adoption of Modern Technologies to Fight the COVID-19 Pandemic: A Technical Synthesis of Latest Developments DOI Creative Commons
Abdul Majeed, Xiaohan Zhang

COVID, Journal Year: 2023, Volume and Issue: 3(1), P. 90 - 123

Published: Jan. 16, 2023

In the ongoing COVID-19 pandemic, digital technologies have played a vital role to minimize spread of COVID-19, and control its pitfalls for general public. Without such technologies, bringing pandemic under would been tricky slow. Consequently, exploration status, devising appropriate mitigation strategies also be difficult. this paper, we present comprehensive analysis community-beneficial that were employed fight pandemic. Specifically, demonstrate practical applications ten major effectively served mankind in different ways during crisis. We chosen these based on their technical significance large-scale adoption arena. The selected are Internet Things (IoT), artificial intelligence(AI), natural language processing(NLP), computer vision (CV), blockchain (BC), federated learning (FL), robotics, tiny machine (TinyML), edge computing (EC), synthetic data (SD). For each technology, working mechanism, context challenges from perspective COVID-19. Our can pave way understanding roles COVID-19-fighting used future infectious diseases prevent global crises. Moreover, discuss heterogeneous significantly contributed addressing multiple aspects when fed aforementioned technologies. To best authors’ knowledge, is pioneering work transformative with broader coverage studies applications.

Language: Английский

Citations

13
Unraveling the Black Box: A Review of Explainable Deep Learning Healthcare Techniques DOI Creative Commons
Nafeesa Yousuf Murad, Mohd Hilmi Hasan, Muhammad Hamza Azam

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 66556 - 66568

Published: Jan. 1, 2024

Language: Английский

Citations

4
Numerical simulation and dynamical system and numerical method for solving biomathematical model with optimal control DOI Creative Commons
A. M. S. Mahdy, Doaa Mohamed

Franklin Open, Journal Year: 2025, Volume and Issue: unknown, P. 100218 - 100218

Published: Jan. 1, 2025

Language: Английский

Citations

0
Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review DOI
Ademola E. Ilesanmi,

Taiwo Ilesanmi,

Babatunde O. Ajayi

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis COVID-19 cases. As imaging technologies have advanced, 3D CNNs emerged as a powerful tool for segmenting classifying in medical images. These demonstrated both high accuracy rapid capabilities, making them crucial effective diagnostics. This study offers thorough review various CNN algorithms, evaluating their efficacy across range modalities. systematically examines recent advancements methodologies. process involved comprehensive screening abstracts titles to ensure relevance, followed by meticulous selection research papers from academic repositories. evaluates these based on specific criteria provides detailed insights into network architectures algorithms used detection. reveals significant trends use segmentation classification. It highlights key findings, including diverse employed compared other diseases, which predominantly utilize encoder/decoder frameworks. an in-depth methods, discussing strengths, limitations, potential areas future research. reviewed total 60 published repositories, Springer Elsevier. this implications clinical diagnosis treatment strategies. Despite some efficiency underscore advancing image findings suggest that could significantly enhance management COVID-19, contributing improved healthcare outcomes.

Language: Английский

Citations

0