Lung Cancer Detection using CNN

Lung cancer remains a sizable global health concern, challenging beforehand detection for improved treatment outcomes. This paper explores the transformative potential of Deep Learning techniques, exclusively convolutional neural networks (CNNs), in the accurate identification and classification of lung cancer using computed tomography (CT) scans. A comprehensive literature survey outlines diverse methodologies, including CNNs, support vector machines (SVMs), and feature extraction, employed in the pursuit of precise lung cancer diagnosis. Our proposed methodology involves preprocessing CT scan images, feature extraction, and the application of a CNN model trained on a dataset of lung CT scans. The CNN model aims to categorize images into distinct types of lung cancer, proposing a promising avenue for enhanced diagnostic accuracy. Results and analysis showcase the proficiency of our CNN model, supported by key performance indicators. As we delve into the future of cancer diagnostics, the integration of advanced machine learning with medical imaging emerges as a beacon of hope for quick and personalized lung cancer detection, surfacing the way for improved patient outcomes.