Radiography image analysis using cat swarm optimized deep belief networks

Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integra...

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Autores principales:	Elameer Amer S., Jaber Mustafa Musa, Abd Sura Khalil
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	radiography images statistical kolmogorov–simonov test cat swarm-optimized deep belief networks adadelta learning process. Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/83b728c7b51b49e88db055f6541b8d37
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id	oai:doaj.org-article:83b728c7b51b49e88db055f6541b8d37
record_format	dspace
spelling	oai:doaj.org-article:83b728c7b51b49e88db055f6541b8d372021-12-05T14:10:51ZRadiography image analysis using cat swarm optimized deep belief networks2191-026X10.1515/jisys-2021-0172https://doaj.org/article/83b728c7b51b49e88db055f6541b8d372021-11-01T00:00:00Zhttps://doi.org/10.1515/jisys-2021-0172https://doaj.org/toc/2191-026XRadiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.Elameer Amer S.Jaber Mustafa MusaAbd Sura KhalilDe Gruyterarticleradiography imagesstatistical kolmogorov–simonov testcat swarm-optimized deep belief networksadadelta learning process.ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 31, Iss 1, Pp 40-54 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiography images statistical kolmogorov–simonov test cat swarm-optimized deep belief networks adadelta learning process. Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	radiography images statistical kolmogorov–simonov test cat swarm-optimized deep belief networks adadelta learning process. Science Q Electronic computers. Computer science QA75.5-76.95 Elameer Amer S. Jaber Mustafa Musa Abd Sura Khalil Radiography image analysis using cat swarm optimized deep belief networks
description	Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies.
format	article
author	Elameer Amer S. Jaber Mustafa Musa Abd Sura Khalil
author_facet	Elameer Amer S. Jaber Mustafa Musa Abd Sura Khalil
author_sort	Elameer Amer S.
title	Radiography image analysis using cat swarm optimized deep belief networks
title_short	Radiography image analysis using cat swarm optimized deep belief networks
title_full	Radiography image analysis using cat swarm optimized deep belief networks
title_fullStr	Radiography image analysis using cat swarm optimized deep belief networks
title_full_unstemmed	Radiography image analysis using cat swarm optimized deep belief networks
title_sort	radiography image analysis using cat swarm optimized deep belief networks
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/83b728c7b51b49e88db055f6541b8d37
work_keys_str_mv	AT elameeramers radiographyimageanalysisusingcatswarmoptimizeddeepbeliefnetworks AT jabermustafamusa radiographyimageanalysisusingcatswarmoptimizeddeepbeliefnetworks AT abdsurakhalil radiographyimageanalysisusingcatswarmoptimizeddeepbeliefnetworks
_version_	1718371675838349312

Radiography image analysis using cat swarm optimized deep belief networks

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