Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI.

Autor: Hussain L; Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.; Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan., Malibari AA; Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia., Alzahrani JS; Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Mecca, Saudi Arabia., Alamgeer M; Department of Information Systems, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi Arabia., Obayya M; Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia., Al-Wesabi FN; Department of Computer Science, College of Science and Art at Mahayil, King Khalid University, Abha, Saudi Arabia., Mohsen H; Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt., Hamza MA; Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia. ma.hamza@psau.edu.sa.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Sep 13; Vol. 12 (1), pp. 15389. Date of Electronic Publication: 2022 Sep 13.
DOI: 10.1038/s41598-022-19563-0
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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