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Effective Evaluation of Medical Images Using Artificial Intelligence Techniques
This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385318/ https://www.ncbi.nlm.nih.gov/pubmed/35990128 http://dx.doi.org/10.1155/2022/8419308 |
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author | Kannan, S. Premalatha, G. Jamuna Rani, M. Jayakumar, D. Senthil, P. Palanivelrajan, S. Devi, S. Sahile, Kibebe |
author_facet | Kannan, S. Premalatha, G. Jamuna Rani, M. Jayakumar, D. Senthil, P. Palanivelrajan, S. Devi, S. Sahile, Kibebe |
author_sort | Kannan, S. |
collection | PubMed |
description | This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson's disease, the affected patients with Parkinson's disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions. |
format | Online Article Text |
id | pubmed-9385318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93853182022-08-18 Effective Evaluation of Medical Images Using Artificial Intelligence Techniques Kannan, S. Premalatha, G. Jamuna Rani, M. Jayakumar, D. Senthil, P. Palanivelrajan, S. Devi, S. Sahile, Kibebe Comput Intell Neurosci Research Article This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson's disease, the affected patients with Parkinson's disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions. Hindawi 2022-08-10 /pmc/articles/PMC9385318/ /pubmed/35990128 http://dx.doi.org/10.1155/2022/8419308 Text en Copyright © 2022 S. Kannan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kannan, S. Premalatha, G. Jamuna Rani, M. Jayakumar, D. Senthil, P. Palanivelrajan, S. Devi, S. Sahile, Kibebe Effective Evaluation of Medical Images Using Artificial Intelligence Techniques |
title | Effective Evaluation of Medical Images Using Artificial Intelligence Techniques |
title_full | Effective Evaluation of Medical Images Using Artificial Intelligence Techniques |
title_fullStr | Effective Evaluation of Medical Images Using Artificial Intelligence Techniques |
title_full_unstemmed | Effective Evaluation of Medical Images Using Artificial Intelligence Techniques |
title_short | Effective Evaluation of Medical Images Using Artificial Intelligence Techniques |
title_sort | effective evaluation of medical images using artificial intelligence techniques |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385318/ https://www.ncbi.nlm.nih.gov/pubmed/35990128 http://dx.doi.org/10.1155/2022/8419308 |
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