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)
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Abstract
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%.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | ANN; CNN models; GLCM; Hemorrhage diseases; Hybrid method; LBP; PCA; SVM |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 07 Feb 2024 07:17 |
| Last Modified: | 07 Feb 2024 07:17 |
| URI: | http://umpir.ump.edu.my/id/eprint/40155 |
| Download Statistic: | View Download Statistics |
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