Performance Analysis of Various Machine Learning and Deep Learning Approaches for the Detection of Lung Cancer

Abstract

Lung cancer is one of the deadliest forms of cancer worldwide, making early detection essential for improving survival outcomes. The use of machine learning (ML) and deep learning (DL) has brought significant advancements to medical diagnostics, providing exceptional accuracy in detecting lung cancer. This text examines the use of ML and DL methods for the detection and classification of lung cancer, emphasizing their effectiveness in analyzing complex medical imaging data, such as CT scans. Sophisticated models, like Principal Component Analysis, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Decision Trees, Artificial Neural Networks, and Deep learning techniques, have shown outstanding performance in identifying lung nodules and differentiating between benign and malignant tumors. These approaches not only improve diagnostic accuracy but also minimize false positives, enabling timely and appropriate medical intervention.