Research and Application of Deep Learning-Based Lung Cancer Diagnosis Models

Lung cancer remains one of the leading causes of cancer-related deaths globally, primarily due to late-stage diagnosis and the inherent complexities of the disease. Traditional diagnostic methods, while effective, often fall short in accuracy and speed, highlighting the need for innovative approaches. This paper investigates the application of deep learning models in lung cancer diagnosis, focusing on Convolutional Neural Networks (CNNs) and their ability to analyze medical images. We explore the architecture, training methodologies, and evaluation metrics of these models, demonstrating their effectiveness compared to conventional techniques. The results indicate that deep learning approaches significantly enhance diagnostic accuracy, leading to improved patient outcomes. This study not only contributes to the existing body of knowledge but also emphasizes the potential of integrating deep learning in clinical workflows for lung cancer detection.