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

作者：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 · 发表于：Heliyon · 年份：2024 · DOI：10.1016/j.heliyon.2024.e26192 · 被引用次数：49 · 研究领域：Lung Cancer Diagnosis and Treatment、COVID-19 diagnosis using AI、AI in cancer detection

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.