Hybrid Method for Detection of Lung Cancer Images using Threshold Technique and KNN Algorithm

Abstract

Lung cancer stands as a formidable adversary in the global fight against cancer-related mortality. Timely detection stands as a linchpin in improving patient prognoses. This paper introduces a pioneering hybrid methodology for the identification of lung cancer imagery, amalgamating threshold-based methodologies with the K-Nearest Neighbors (KNN) algorithm. Our proposed approach is designed to bolster the precision and efficacy of lung cancer detection by amalgamating techniques from both image segmentation and machine learning domains. We meticulously conduct a battery of experiments to meticulously scrutinize and validate the efficacy of our novel methodology against established techniques, substantiating its prowess in precisely delineating lung cancer regions within medical imagery.