An Empirical Study on Lung Cancer Detection and Classification Using Machine Learning and Image Processing Techniques

Lung cancer is a significant health issue, causing around one million deaths annually. CT scan images of the chest are essential for the early diagnosis of lung nodules, particularly considering the rising number of lung nodules. Integrating Computer-Aided Diagnosis (CAD) systems has become essential to increase the accuracy and promptness of lung cancer prognosis. While traditional methods have provided valuable insights, advanced machine learning and image processing techniques, including Generative AI, hold immense promise for revolutionizing lung cancer diagnosis. Generative Adversarial Networks (GANs), a type of Generative AI, can create synthetic medical images that mimic real CT scans with lung nodules. This innovation tackles a major hurdle in medical AI—the limited availability of high-quality, labeled data. By enriching training datasets with these synthetic images, Convolutional Neural Networks (CNNs) can be trained on a broader and more diverse range of data, improving their ability to recognize subtle abnormalities indicative of early-stage lung cancer. Furthermore, Generative AI offers the potential to create synthetic images with specific characteristics, allowing researchers to explore CNN behaviour under various scenarios and refine their robustness in diagnosing different lung cancer subtypes.