Evaluating Lung Cancer Classification Performance Using Multiple Feature Extraction Methods with SVM and KNN Classifiers
Abstract
Lung cancer is one of the most prevalent causes of mortality worldwide, making early detection essential for improving patient survival rates. Computed tomography (CT) imaging serves as a crucial diagnostic tool; however, the large volume of generated images poses challenges in precise interpretation by radiologists. This study evaluates the effectiveness of lung cancer classification by utilizing various feature extraction techniques in combination with support vector machine (SVM) and k-nearest neighbours (KNN) classifiers. By analysing different feature sets, the research aims to identify the most effective combination for enhanced classification accuracy. The findings indicate notable improvements in classification performance, facilitating more reliable lung cancer detection.
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