Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma

The escalating mortality rate attributed to lung cancer is exacerbated by its inflammatory nature, fostering uncontrolled cellular proliferation within lung tissues. While complete eradication remains a challenge, proactive screening holds promise for mitigating its impact. This study is dedicated to exploring a repertoire of machine learning classification methodologies, with a primary focus on identifying effective approaches. Leveraging advanced imaging techniques, particularly CT images, this study aims to ascertain the most reliable means of diagnosing lung cancer. Through rigorous comparison and evaluation, we endeavor to detail comparisons of machine learning algorithms on effective detection frameworks that can significantly enhance screening protocols.