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

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, stren...

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Bibliographic Details
Published in:	Scientific reports Vol. 12; no. 1; p. 15389
Main Authors:	Hussain, Lal, Malibari, Areej A., Alzahrani, Jaber S., Alamgeer, Mohamed, Obayya, Marwa, Al-Wesabi, Fahd N., Mohsen, Heba, Hamza, Manar Ahmed
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 13-09-2022 Nature Publishing Group Nature Portfolio
Subjects:	631/67 692/700 Bayesian analysis Brain cancer Brain tumors Deep learning Entropy Humanities and Social Sciences Meningioma multidisciplinary Pituitary Prognosis Science Science (multidisciplinary)
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-022-19563-0