Abstract
Lung cancer is the most common type of cancer among males worldwide. It accounts for one of every five cancer-related fatalities and is prevalent in people aged 55 to 65. Detecting lung cancer in its earliest stages is a crucial step in the treatment process that can significantly increase the chance of survival. In this paper, we used image processing techniques with MATLAB on computed tomography (CT) images of lung cancer for multiple patients to determine the location and extent of cancerous spots. The stages included image analysis and segmentation, feature extraction, and candidate identification as distinct regions of interest (ROI). Algorithms based on machine learning were utilized to classify cancer from the ROIs of candidates by extracting the characteristics required for the classification of pathologic features from the annotated ROIs. Comparing the evaluated algorithms in order to identify the optimal algorithm for cancer detection.