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Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amount...

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Main Authors: Mohammed, Badiea Abdulkarem, Senan, Ebrahim Mohammed, Al-Mekhlafi, Zeyad Ghaleb, Rassem, Taha Hussein, Makbol, Nasrin M., Alanazi, Adwan Alownie, Almurayziq, Tariq S., Ghaleb, Fuad A., Sallam, Amer A.
Format: Article
Language:English
Published: MDPI 2022
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40155/1/Multi-method%20diagnosis%20of%20CT%20Images%20for%20rapid%20detection%20of%20intracranial.pdf
http://umpir.ump.edu.my/id/eprint/40155/
https://doi.org/10.3390/electronics11152460
https://doi.org/10.3390/electronics11152460
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spelling my.ump.umpir.401552024-02-07T07:17:58Z http://umpir.ump.edu.my/id/eprint/40155/ Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning Mohammed, Badiea Abdulkarem Senan, Ebrahim Mohammed Al-Mekhlafi, Zeyad Ghaleb Rassem, Taha Hussein Makbol, Nasrin M. Alanazi, Adwan Alownie Almurayziq, Tariq S. Ghaleb, Fuad A. Sallam, Amer A. QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%. MDPI 2022-08 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/40155/1/Multi-method%20diagnosis%20of%20CT%20Images%20for%20rapid%20detection%20of%20intracranial.pdf Mohammed, Badiea Abdulkarem and Senan, Ebrahim Mohammed and Al-Mekhlafi, Zeyad Ghaleb and Rassem, Taha Hussein and Makbol, Nasrin M. and Alanazi, Adwan Alownie and Almurayziq, Tariq S. and Ghaleb, Fuad A. and Sallam, Amer A. (2022) Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning. Electronics (Switzerland), 11 (2460). ISSN 2079-9292. (Published) https://doi.org/10.3390/electronics11152460 https://doi.org/10.3390/electronics11152460
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Mohammed, Badiea Abdulkarem
Senan, Ebrahim Mohammed
Al-Mekhlafi, Zeyad Ghaleb
Rassem, Taha Hussein
Makbol, Nasrin M.
Alanazi, Adwan Alownie
Almurayziq, Tariq S.
Ghaleb, Fuad A.
Sallam, Amer A.
Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
description Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%.
format Article
author Mohammed, Badiea Abdulkarem
Senan, Ebrahim Mohammed
Al-Mekhlafi, Zeyad Ghaleb
Rassem, Taha Hussein
Makbol, Nasrin M.
Alanazi, Adwan Alownie
Almurayziq, Tariq S.
Ghaleb, Fuad A.
Sallam, Amer A.
author_facet Mohammed, Badiea Abdulkarem
Senan, Ebrahim Mohammed
Al-Mekhlafi, Zeyad Ghaleb
Rassem, Taha Hussein
Makbol, Nasrin M.
Alanazi, Adwan Alownie
Almurayziq, Tariq S.
Ghaleb, Fuad A.
Sallam, Amer A.
author_sort Mohammed, Badiea Abdulkarem
title Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
title_short Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
title_full Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
title_fullStr Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
title_full_unstemmed Multi-method diagnosis of CT images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
title_sort multi-method diagnosis of ct images for rapid detection of intracranial hemorrhages based on deep and hybrid learning
publisher MDPI
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/40155/1/Multi-method%20diagnosis%20of%20CT%20Images%20for%20rapid%20detection%20of%20intracranial.pdf
http://umpir.ump.edu.my/id/eprint/40155/
https://doi.org/10.3390/electronics11152460
https://doi.org/10.3390/electronics11152460
_version_ 1822924119166943232
score 13.239859

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