Li, Liangyu and Yang, Jing and Por, Lip Yee and Khan, Mohammad Shahbaz and Hamdaoui, Rim and Hussain, Lal and Iqbal, Zahoor and Rotaru, Ionela Magdalena and Dobrota, Dan and Aldrdery, Moutaz and Omar, Abdulfattah (2024) Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques. Heliyon, 10 (4). e26192. ISSN 2405-8440, DOI https://doi.org/10.1016/j.heliyon.2024.e26192.
Full text not available from this repository.Abstract
Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level cooccurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.
| Item Type: | Article |
|---|---|
| Funders: | King Khalid University, King Khalid University King Saud University (R.G.P. 2/57/44), Deanship of Scientific Research at Shaqra University, Prince Sattam Bin Abdulaziz University (PSAU/2023/R/1445) |
| Uncontrolled Keywords: | Classification; Haralick texture features; Lung cancer types; Autoencoder and gray-level co-occurrence; (GLCM) |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Nov 2024 02:30 |
| Last Modified: | 06 Nov 2024 02:30 |
| URI: | http://eprints.um.edu.my/id/eprint/45598 |
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