Epileptic Seizures Detection Using Deep Learning Techniques: A Review

International Journal of Environmental Research and Public Health, Journal Year: 2021, Volume and Issue: 18(11), P. 5780 - 5780

Published: May 27, 2021

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a areas, one its branches is deep learning (DL). Before the rise DL, conventional machine algorithms involving feature extraction were performed. This limited their performance ability those handcrafting features. However, in features classification are entirely automated. The advent these techniques many areas medicine, such as diagnosis has made significant advances. In this study, comprehensive overview works focused on automated seizure detection DL neuroimaging modalities presented. Various methods seizures automatically EEG MRI described. addition, rehabilitation systems developed for analyzed, summary provided. tools include cloud computing hardware required implementation algorithms. important challenges accurate with discussed. advantages limitations employing DL-based Finally, most promising models possible future delineated.

Language: Английский

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Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 139, P. 104949 - 104949

Published: Oct. 29, 2021

Language: Английский

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A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images

Biomedical Signal Processing and Control, Journal Year: 2021, Volume and Issue: 71, P. 103182 - 103182

Published: Sept. 23, 2021

Language: Английский

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Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 136, P. 104697 - 104697

Published: July 31, 2021

Language: Английский

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Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Frontiers in Molecular Neuroscience, Journal Year: 2022, Volume and Issue: 15

Published: Oct. 4, 2022

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD also associated with communication deficits repetitive behavior affected individuals. Various detection methods have been developed, including neuroimaging modalities psychological tests. Among these methods, magnetic resonance imaging (MRI) are of paramount importance to physicians. Clinicians rely on MRI diagnose accurately. The non-invasive include functional (fMRI) structural (sMRI) methods. However, diagnosing fMRI sMRI for specialists often laborious time-consuming; therefore, several computer-aided design systems (CADS) based artificial intelligence (AI) developed assist specialist Conventional machine learning (ML) deep (DL) the most popular schemes AI used ASD. This study aims review automated using AI. We CADS ML techniques diagnosis modalities. There has very limited work use DL develop diagnostic models A summary studies provided Supplementary Appendix. Then, challenges encountered during described detail. Additionally, graphical comparison automatically discussed. suggest future approaches detecting ASDs neuroimaging.

Language: Английский

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

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

Published: Jan. 26, 2024

Language: Английский

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Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography

Journal of Digital Imaging, Journal Year: 2022, Volume and Issue: 35(6), P. 1445 - 1462

Published: July 11, 2022

Language: Английский

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Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Computer Methods and Programs in Biomedicine, Journal Year: 2022, Volume and Issue: 218, P. 106731 - 106731

Published: March 5, 2022

Language: Английский

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Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare

Medicine Advances, Journal Year: 2024, Volume and Issue: 2(3), P. 205 - 237

Published: Aug. 27, 2024

Abstract Machine learning (ML) has achieved substantial success in performing healthcare tasks which the configuration of every part ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use techniques, Automated (AutoML) emerged as a prospective solution. However, most models generated by AutoML are black boxes that challenging comprehend and deploy settings. We conducted systematic review examine interpretation systems for healthcare. searched four databases (MEDLINE, EMBASE, Web Science, Scopus) complemented seven prestigious conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, NeurIPS) reported before September 1, 2023. included 118 articles related First, we illustrated techniques used publications, including automated data preparation, feature engineering, model development, accompanied real‐world case study demonstrate advantages over classic ML. Then, summarized methods: interaction importance, dimensionality reduction, intrinsically interpretable models, knowledge distillation rule extraction. Finally, detailed how been six major types: image, free text, tabular data, signal, genomic sequences, multi‐modality. some extent, provides effortless development improves users' trust In future studies, researchers should explore seamless integration automation interpretation, compatibility multi‐modality, utilization foundation models.

Language: Английский

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An intellectual autism spectrum disorder classification framework in healthcare industry using ViT-based adaptive deep learning model

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107737 - 107737

Published: March 3, 2025

Language: Английский

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