Lung Cancer Detection Using Deep Learning

Abstract: Lung cancer remains a significant global health challenge, with early and accurate detection being crucial for improving survival rates. Current diagnostic methods, includ-ing Computer-Aided Diagnosis (CAD) systems, often rely on semiautomatic processes that require substantial input from radiologists, leading to variability and delays in diagnosis. This paper introduces an End-to-End Fully Automated Lung Cancer Screening System designed to address these challenges. The proposed system integrates five key modules: abnormality detection, cancer segmentation, volume estimation, cancer grading, and an earlywarningsystem.Anovelmodifiedconvolutiontechnique is employed to enhance boundary retention and accuracy in segmentation,achievingasegmentationaccuracyof92.09%.The volume estimation model utilizes Gaussian Process Regression (GPR), improving accuracy to 94.18%, while the grading model follows the TNM classification, reaching an accuracy of 96.4%. Theearlywarningmoduleprovidesreal-timealertsforchangesin patient conditions, facilitating timely interventions. This holistic approach aims to streamline lung cancer screening, reduce diagnostic delays, and improve patient outcomes.