Mohammed, B. A., Senan, E. M., Al-Mekhlafi, Z. G., Rassem, T. H., Makbol, N. M., Alanazi, A. A., Almurayziq, T. S., Ghaleb, F. A. and Sallam, A. A., 2022. Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning. Electronics, 11 (15), 2460.
Full text available as:
|
PDF (OPEN ACCESS ARTICLE)
electronics-11-02460-v2.pdf - Published Version Available under License Creative Commons Attribution. 4MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
DOI: 10.3390/electronics11152460
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 |
---|---|
ISSN: | 2079-9292 |
Additional Information: | (This article belongs to the Section Computer Science & Engineering) |
Uncontrolled Keywords: | CNN models; hybrid method; ANN; SVM; hemorrhage diseases; LBP; GLCM; PCA |
Group: | Faculty of Science & Technology |
ID Code: | 37410 |
Deposited By: | Symplectic RT2 |
Deposited On: | 01 Sep 2022 09:40 |
Last Modified: | 01 Sep 2022 09:40 |
Downloads
Downloads per month over past year
Repository Staff Only - |