Yang, Jing and Yee, Por Lip and Khan, Abdullah Ayub and Karamti, Hanen and Eldin, Elsayed Tag and Aldweesh, Amjad and El Jery, Atef and Hussain, Lal and Omar, Abdulfattah (2023) 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. Digital Health, 9. ISSN 2055-2076, DOI https://doi.org/10.1177/20552076231172632.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Machine learning; small cell lung cancer; non-small cell lung cancer (NSCLC); Cluster Prominence; Bayesian method |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Oct 2025 07:33 |
| Last Modified: | 30 Oct 2025 07:33 |
| URI: | http://eprints.um.edu.my/id/eprint/50001 |
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