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

Jing Yang; Por Lip Yee; Abdullah Ayub Khan; Hanen Karamti; Elsayed Tag Eldin; Amjad Aldweesh; Atef El Jery; Lal Hussain; Abdulfattah Omar

doi:10.1177/20552076231172632

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

Digit Health. 2023 May 25:9:20552076231172632. doi: 10.1177/20552076231172632. eCollection 2023 Jan-Dec.

Authors

Jing Yang¹, Por Lip Yee¹, Abdullah Ayub Khan², Hanen Karamti³, Elsayed Tag Eldin⁴, Amjad Aldweesh⁵, Atef El Jery^{6

7}, Lal Hussain^{8

9}, Abdulfattah Omar¹⁰

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 National Engineering School of Gabes, Gabes University, Zrig Gabes, Tunisia.
⁸ Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan.
⁹ Department of Computer Science and Information Technology, University of Azad Jammu and Kashmir, Athmuqam, Azad Kashmir, Pakistan.
¹⁰ Department of English, College of Science & Humanities, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.

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.

Keywords: Bayesian method; Cluster Prominence; Machine learning; non-small cell lung cancer (NSCLC); small cell lung cancer.