Skip to main content

Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning.

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:

electronics-11-02460-v2.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.3390/electronics11152460


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:(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 per month over past year

More statistics for this item...
Repository Staff Only -