AUTHOR=Oyediran Mayowa O. , Ojo Olufemi S. , Raji Ibrahim A. , Adeniyi Abidemi Emmanuel , Aroba Oluwasegun Julius TITLE=An optimized support vector machine for lung cancer classification system JOURNAL=Frontiers in Oncology VOLUME=14 YEAR=2024 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1408199 DOI=10.3389/fonc.2024.1408199 ISSN=2234-943X ABSTRACT=

Lung cancer is one of the main causes of the rising death rate among the expanding population. For lung cancer patients to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of this research is to enhance machine learning to increase the precision and quality of lung cancer classification. The dataset was obtained from an open-source database and was utilized for testing and training. The suggested system used a CT scan picture as its input image, and it underwent a variety of image processing operations on it, including segmentation, contrast enhancement, and feature extraction. The training process produces a chameleon Swarm Based Support Vector Machine that can identify between benign, malignant, and normal nodules. The performance of the system is evaluated in terms of false positive rate, sensitivity, specificity accuracy and recognition time.