Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques

Liangyu Li; Jing Yang; Lip Yee Por; Mohammad Shahbaz Khan; Rim Hamdaoui; Lal Hussain; Zahoor Iqbal; Ionela Magdalena Rotaru; Dan Dobrotă; Moutaz Aldrdery; Abdulfattah Omar

doi:10.1016/j.heliyon.2024.e26192

Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques

Heliyon. 2024 Feb 14;10(4):e26192. doi: 10.1016/j.heliyon.2024.e26192. eCollection 2024 Feb 29.

Authors

Liangyu Li^{1

2}, Jing Yang³, Lip Yee Por³, Mohammad Shahbaz Khan⁴, Rim Hamdaoui⁵, Lal Hussain^{6

7}, Zahoor Iqbal⁸, Ionela Magdalena Rotaru⁹, Dan Dobrotă¹⁰, Moutaz Aldrdery¹¹, Abdulfattah Omar¹²

Affiliations

¹ Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
² Health Informatics Laboratory, Cancer Research Institute, Chifeng Cancer Hospital (Second Affiliated Hospital of Chifeng University), Medical Department, Chifeng University, Chifeng City, Inner Mongolia Autonomous Region, 024000, China.
³ Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
⁴ Children's National Hospital, 111 Michigan Ave NW, Washington, DC, 20010, United States.
⁵ Department of Computer Science, College of Science and Human Studies Dawadmi, Shaqra University, Shaqra, Riyadh, Saudi Arabia.
⁶ Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan.
⁷ Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan.
⁸ School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China.
⁹ Department of Industrial Engineering and Management, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania.
¹⁰ Faculty of Engineering, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, Sibiu, 550024, Romania.
¹¹ Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia.
¹² Department of English, College of Science & Humanities, Prince Sattam Bin Abdulaziz University, Saudi Arabia.

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 co-occurrence 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: Autoencoder and gray-level co-occurrence (GLCM); Classification; Haralick texture features; Lung cancer types.