BayesiaLab
Bayesian Dynamic Profiling and Optimization of Important Ranked Energy from Gray-level Co-occurrence

Bayesian Dynamic Profiling and Optimization of Important Ranked Energy from Gray-level Co-occurrence

Presented at the 2023 BayesiaLab Conference.

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 features to analyze a comprehensive analysis among extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. This study is focused to utilize the important highly ranked energy feature as the target node for empirical analysis of dynamic profiling and optimization to unfold the nonlinear dynamics of GLCM features extracted from Brain MRIs to distinguish the pituitary and meningioma glands.The highest strength of relationship was obtained between the nodes (Correlation→correlation2) yielding strength of relationship using KL and MI (1.4640), Pearson’s correlation (1.0000), with relative width 1.0000 and overall contribution of 16.67%. The segment profile analysis of top ranked target node with other extracted GLCM features using the Radar chart was computed which reflect the distributions based on 1 to 12 clock hours. We used the NHST t-test and Bayesian test to find the significance to distinguish with other different states such as <= 0.273, c <= 0.368, d <= 0.471, e > 0.471. The clusters <= 0.273, <= 0.471 and > 0.471 using both the test yielded the highly significant results with all the extracted GLCM features. The state <= 0.368 yielded high significant results using both test with homogenity1, dissimilarity, correlation, correlation2, and autocorrelation, whereas significant results using NHST t-test with contrast and energy. Using the tornado graph, we visualize the maximum deltas in the posterior probabilities of the target states, and hard evidence is set on the selected variables. The highest association was yielded with entropy, homogeneity, dissimilarity, contrast, correlation, correlation2 cluster state <= 0.273 followed by cluster state <= 0.368, <= 0.471, and > 0.471. This indicates that high top ranked Energy feature prevails high associations with entropy, homogeneity, dissimilarity, contrast, correlation, and correlation which can be used as a better predictor for improved diagnosis and prognosis of brain tumor types. Previous studies rely on classification methods. However, this novel technique is proposed to further investigate the dynamics, associations, posterior probabilities, prior probabilities, marginal likelihood, prior means, and posterior means to further unfold the relevance and relationships among the extracted features. The proposed approach will be very helpful for improved diagnosis and prognosis of brain tumor types. The proposed method further unfolds the dynamics and detailed analysis of computed features based on GLCM features for a better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

Presentation Video

About the Presenter

Dr. Lal Hussain is an Assistant Professor at the Department of Computer Science & IT, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan. He obtained his MS in Communication and Networks from Iqra University, Islamabad, Pakistan, in 2012 with a gold medal. He received Ph.D. from the Department of Computer Science & Information Technology, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan in February 2016. He worked as visiting Ph.D. researcher at Lancaster University UK for six months under HEC International Research Initiative Program and worked under the supervision of Dr. Aneta Stefanovska, Professor of Biomedical Physics, Physics Department, Lancaster University, during 2014-2015 in the UK. Dr. Hussain recently completed a one-year Postdoctoral fellowship from Montefiore Medical Center and Albert Einstein College of Medicine, New York, USA, under the supervision of Dr. Tim Q Duong, Professor and Vice Chair of MRI research. He also worked at Duong Lab at Stony Brook University USA on different ongoing projects with Dr. Duong from January-March 2020. Recently, Elsevier ranked Dr. Hussain in the top-1% of the world scientists list of 2021 based on their research record. He is the author of more than 50 publications of highly reputed peer-reviewed and Impact Fact Journals as Principal author. He completed various funded projects as PI and Co-PI from Ignite, ICT Pakistan, and the University of Jeddah, Saudi Electronic University, Kingdom of Saudi Arabia. He presented various talks in Pakistan, the UK, Peru, and the USA. His research interest includes developing and optimizing AI tools, including machine learning, deep learning, and neural network algorithms, feature extraction and selection methods, information-theoretic methods, time-frequency representation methods, and cross-frequency coupling to predict disease severity, progression, survival, and recurrence. His area of interest includes biomedical signal and image processing problems, including prostate cancer, breast cancer, lung cancer, brain tumor, covid-19 lung infection with different modalities (i.e., MRI, CT, X-Ray, etc.), brain dynamics and diseases (i.e., autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), Alzheimer's Disease).


For North America

Bayesia USA

4235 Hillsboro Pike
Suite 300-688
Nashville, TN 37215, USA

+1 888-386-8383
info@bayesia.us

Head Office

Bayesia S.A.S.

Parc Ceres, Batiment N 21
rue Ferdinand Buisson
53810 Change, France

For Asia/Pacific

Bayesia Singapore

1 Fusionopolis Place
#03-20 Galaxis
Singapore 138522


Copyright © 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.