Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI
Abstract Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevan...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-19563-0 |
| _version_ | 1818020392022835200 |
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| author | Lal Hussain Areej A. Malibari Jaber S. Alzahrani Mohamed Alamgeer Marwa Obayya Fahd N. Al-Wesabi Heba Mohsen Manar Ahmed Hamza |
| author_facet | Lal Hussain Areej A. Malibari Jaber S. Alzahrani Mohamed Alamgeer Marwa Obayya Fahd N. Al-Wesabi Heba Mohsen Manar Ahmed Hamza |
| author_sort | Lal Hussain |
| collection | DOAJ |
| description | Abstract Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke. |
| first_indexed | 2024-04-14T08:05:22Z |
| format | Article |
| id | doaj.art-7971eecb3bde4a54b1349b904f44a929 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-14T08:05:22Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-7971eecb3bde4a54b1349b904f44a9292022-12-22T02:04:47ZengNature PortfolioScientific Reports2045-23222022-09-0112111910.1038/s41598-022-19563-0Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRILal Hussain0Areej A. Malibari1Jaber S. Alzahrani2Mohamed Alamgeer3Marwa Obayya4Fahd N. Al-Wesabi5Heba Mohsen6Manar Ahmed Hamza7Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and KashmirDepartment of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman UniversityDepartment of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura UniversityDepartment of Information Systems, College of Science and Art at Mahayil, King Khalid UniversityDepartment of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman UniversityDepartment of Computer Science, College of Science and Art at Mahayil, King Khalid UniversityDepartment of Computer Science, Faculty of Computers and Information Technology, Future University in EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz UniversityAbstract Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.https://doi.org/10.1038/s41598-022-19563-0 |
| spellingShingle | Lal Hussain Areej A. Malibari Jaber S. Alzahrani Mohamed Alamgeer Marwa Obayya Fahd N. Al-Wesabi Heba Mohsen Manar Ahmed Hamza Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI Scientific Reports |
| title | Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI |
| title_full | Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI |
| title_fullStr | Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI |
| title_full_unstemmed | Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI |
| title_short | Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI |
| title_sort | bayesian dynamic profiling and optimization of important ranked energy from gray level co occurrence glcm features for empirical analysis of brain mri |
| url | https://doi.org/10.1038/s41598-022-19563-0 |
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