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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/15/2460 |
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| author | Badiea Abdulkarem Mohammed Ebrahim Mohammed Senan Zeyad Ghaleb Al-Mekhlafi Taha H. Rassem Nasrin M. Makbol Adwan Alownie Alanazi Tariq S. Almurayziq Fuad A. Ghaleb Amer A. Sallam |
| author_facet | Badiea Abdulkarem Mohammed Ebrahim Mohammed Senan Zeyad Ghaleb Al-Mekhlafi Taha H. Rassem Nasrin M. Makbol Adwan Alownie Alanazi Tariq S. Almurayziq Fuad A. Ghaleb Amer A. Sallam |
| author_sort | Badiea Abdulkarem Mohammed |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-09T10:08:47Z |
| format | Article |
| id | doaj.art-a5ff8ce77b5d4190a6dc4346104bcb68 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T10:08:47Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a5ff8ce77b5d4190a6dc4346104bcb682023-12-01T22:54:12ZengMDPI AGElectronics2079-92922022-08-011115246010.3390/electronics11152460Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid LearningBadiea Abdulkarem Mohammed0Ebrahim Mohammed Senan1Zeyad Ghaleb Al-Mekhlafi2Taha H. Rassem3Nasrin M. Makbol4Adwan Alownie Alanazi5Tariq S. Almurayziq6Fuad A. Ghaleb7Amer A. Sallam8Department of Computer Engineering, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, IndiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaFaculty of Science and Technology, Bournemouth University, Poole BH12 5BB, UKCentre for Software Development & Integrated Computing (Software Centre), Universiti Malaysia Pahang, Gambang 26300, MalaysiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaDepartment of Information and Computer Science, College of Computer Science and Engineering, University of Ha’il, Ha’il 81481, Saudi ArabiaSchool of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaEngineering and Information Technology College, Taiz University, Taiz 6803, YemenIntracranial 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%.https://www.mdpi.com/2079-9292/11/15/2460CNN modelshybrid methodANNSVMhemorrhage diseasesLBP |
| spellingShingle | Badiea Abdulkarem Mohammed Ebrahim Mohammed Senan Zeyad Ghaleb Al-Mekhlafi Taha H. Rassem Nasrin M. Makbol Adwan Alownie Alanazi Tariq S. Almurayziq Fuad A. Ghaleb Amer A. Sallam Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning Electronics CNN models hybrid method ANN SVM hemorrhage diseases LBP |
| title | 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_short | 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 |
| topic | CNN models hybrid method ANN SVM hemorrhage diseases LBP |
| url | https://www.mdpi.com/2079-9292/11/15/2460 |
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