CT scan Image Enhancement For Liver Cancer Detection

Liver cancer remains one of the most common and deadly forms of cancer worldwide, posing significant diagnostic challenges due to factors like low image contrast, noise, and the complex anatomical structure of the liver. This study explores the application of Generative Adversarial Networks (GANs) for enhancing computed tomography (CT) images to improve liver cancer detection. By leveraging the capabilities of GANs, the proposed system enhances low-contrast images, reduces noise, and preserves critical structural details, facilitating better tumour segmentation and localization. The GAN-enhanced imaging system is integrated into a web-based platform that supports real-time image analysis and classification, distinguishing between malignant and normal liver tissues. This approach addresses the limitations of traditional diagnostic methods, potentially revolutionizing liver cancer diagnosis by enabling earlier detection and more accurate treatment planning. The results from applying GANs demonstrate significant improvements in image quality and diagnostic accuracy, offering a scalable and accessible solution for medical professionals worldwide. This research highlights the potential of AI-driven technologies in transforming liver cancer diagnostics, suggesting broader implications for various medical imaging applications.