LUNG CANCER DETECTION SYSTEM

Abstract

Lung cancer continues to be the world's biggest cancer killer, with a projected 1.8 million deaths every year, based on the World Health Organization (WHO). There has been significant progress in management by therapy, but the prognosis is bad with delayed presentation. Early diagnosis is thus still a priority in reducing mortality and enhancing patient outcome.The objectives of this paper are to examine detection methods for lung cancer, enumerate the insufficiency of existing methods, and suggest the futureThe process is by way of reading articles between 2010 and 2025 of peer-reviewed research on imaging techniques, machine learning techniques, and biomarker-based techniques. The findings are that deaths are lowered by 20% by LDCT, AI models enhance accuracy in the classification of nodules, and biomarkers are potential instruments as add-ons in early detection. Despite this, barriers in the form of false positives, explanation of AI models, and ethical issues exist.This research concludes that the multimodal paradigm of medical imaging, AI, and biomarkers is the most promising method for early detection. Suggested propositions are the development of explainable AI, proof of biomarker-based techniques, and incorporation of multimodal screening into healthcare systems. Drawbacks are the use of secondary literature and no experimental data.