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Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features

Yang J 1 , Yee PL 1 , Khan AA 2 , Karamti H 3 , Eldin ET 4 , Aldweesh A 5 Show all authors , Jery AE 6 , Hussain L 7 , Omar A 8

Affiliations 

  • 1 Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
  • 2 Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, Pakistan
  • 3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 4 Faculty of Engineering and Technology, Future University in Egypt, New Cairo, Cairo, Egypt
  • 5 College of Computer Science and Information Technology, Shaqra University, Shaqra, Saudi Arabia
  • 6 Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia
  • 7 Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
  • 8 Department of English, College of Science & Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Digit Health, 2023;9:20552076231172632.
PMID: 37256015 DOI: 10.1177/20552076231172632

Abstract

Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. 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 lung cancer.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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