Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging
Intracranial haemorrhage (ICH) has become a critical healthcare emergency that needs accurate assessment and earlier diagnosis. Due to the high rates of mortality (about 40%), the early classification and detection of diseases through computed tomography (CT) images were needed to guarant...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10188680/ |
| _version_ | 1797771701114634240 |
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| author | Noha Negm Ghadah Aldehim Faisal Mohammed Nafie Radwa Marzouk Mohammed Assiri Mohamed Ibrahim Alsaid Suhanda Drar Sitelbanat Abdelbagi |
| author_facet | Noha Negm Ghadah Aldehim Faisal Mohammed Nafie Radwa Marzouk Mohammed Assiri Mohamed Ibrahim Alsaid Suhanda Drar Sitelbanat Abdelbagi |
| author_sort | Noha Negm |
| collection | DOAJ |
| description | Intracranial haemorrhage (ICH) has become a critical healthcare emergency that needs accurate assessment and earlier diagnosis. Due to the high rates of mortality (about 40%), the early classification and detection of diseases through computed tomography (CT) images were needed to guarantee a better prognosis and control the occurrence of neurologic deficiencies. Generally, in the earlier diagnoses test for severe ICH, CT imaging of the brain was implemented in the emergency department. Meanwhile, manual diagnoses are labour-intensive, and automatic ICH recognition and classification techniques utilizing artificial intelligence (AI) models are needed. Therefore, the study presents an Intracranial Haemorrhage Diagnosis using Willow Catkin Optimization with Voting Ensemble (ICHD-WCOVE) Model on CT images. The presented ICHD-WCOVE technique exploits computer vision and ensemble learning techniques for automated ICH classification. The presented ICHD-WCOVE technique involves the design of a multi-head attention-based CNN (MAFNet) model for feature vector generation with optimal hyperparameter tuning using the WCO algorithm. For automated ICH detection and classification, the majority voting ensemble deep learning (MVEDL) technique is used, which comprises recurrent neural network (RNN), Bi-directional long short-term memory (BiLSTM), and extreme learning machine-stacked autoencoder (ELM-SAE). The experimental analysis of the ICHD-WCOVE approach can be tested by a medical dataset and the outcomes signified the betterment of the ICHD-WCOVE technique over other existing approaches. |
| first_indexed | 2024-03-12T21:40:24Z |
| format | Article |
| id | doaj.art-6853e26b8e42453d9b4ea73d52a6226d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:40:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6853e26b8e42453d9b4ea73d52a6226d2023-07-26T23:00:37ZengIEEEIEEE Access2169-35362023-01-0111754747548310.1109/ACCESS.2023.329728110188680Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain ImagingNoha Negm0https://orcid.org/0009-0005-5911-1033Ghadah Aldehim1Faisal Mohammed Nafie2https://orcid.org/0000-0002-0870-3507Radwa Marzouk3https://orcid.org/0000-0001-6527-9856Mohammed Assiri4https://orcid.org/0000-0002-6367-2977Mohamed Ibrahim Alsaid5Suhanda Drar6Sitelbanat Abdelbagi7Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Natural and Applied Sciences, Community College, Majmaah University, Al Majma’ah, Saudi ArabiaDepartment of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi ArabiaDepartment of Computer Science, Al-Aflaj College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al-Aflaj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaIntracranial haemorrhage (ICH) has become a critical healthcare emergency that needs accurate assessment and earlier diagnosis. Due to the high rates of mortality (about 40%), the early classification and detection of diseases through computed tomography (CT) images were needed to guarantee a better prognosis and control the occurrence of neurologic deficiencies. Generally, in the earlier diagnoses test for severe ICH, CT imaging of the brain was implemented in the emergency department. Meanwhile, manual diagnoses are labour-intensive, and automatic ICH recognition and classification techniques utilizing artificial intelligence (AI) models are needed. Therefore, the study presents an Intracranial Haemorrhage Diagnosis using Willow Catkin Optimization with Voting Ensemble (ICHD-WCOVE) Model on CT images. The presented ICHD-WCOVE technique exploits computer vision and ensemble learning techniques for automated ICH classification. The presented ICHD-WCOVE technique involves the design of a multi-head attention-based CNN (MAFNet) model for feature vector generation with optimal hyperparameter tuning using the WCO algorithm. For automated ICH detection and classification, the majority voting ensemble deep learning (MVEDL) technique is used, which comprises recurrent neural network (RNN), Bi-directional long short-term memory (BiLSTM), and extreme learning machine-stacked autoencoder (ELM-SAE). The experimental analysis of the ICHD-WCOVE approach can be tested by a medical dataset and the outcomes signified the betterment of the ICHD-WCOVE technique over other existing approaches.https://ieeexplore.ieee.org/document/10188680/Brain imagingintracranial haemorrhagedeep learningcomputer visionensemble learningwillow catkin optimization |
| spellingShingle | Noha Negm Ghadah Aldehim Faisal Mohammed Nafie Radwa Marzouk Mohammed Assiri Mohamed Ibrahim Alsaid Suhanda Drar Sitelbanat Abdelbagi Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging IEEE Access Brain imaging intracranial haemorrhage deep learning computer vision ensemble learning willow catkin optimization |
| title | Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging |
| title_full | Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging |
| title_fullStr | Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging |
| title_full_unstemmed | Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging |
| title_short | Intracranial Haemorrhage Diagnosis Using Willow Catkin Optimization With Voting Ensemble Deep Learning on CT Brain Imaging |
| title_sort | intracranial haemorrhage diagnosis using willow catkin optimization with voting ensemble deep learning on ct brain imaging |
| topic | Brain imaging intracranial haemorrhage deep learning computer vision ensemble learning willow catkin optimization |
| url | https://ieeexplore.ieee.org/document/10188680/ |
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