Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques
Document Type
Article
Publication Date
2-1-2024
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.
Keywords
Classification, Haralick texture features, Lung cancer types, Autoencoder and gray-level co-occurrence, (GLCM)
Divisions
fsktm
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)
Publication Title
Heliyon
Volume
10
Issue
4
Publisher
Elsevier
Publisher Location
50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA