Lung cancer is a major global cause of cancer-related deaths, emphasizing the importance of early detection through chest imaging. Accurate reconstruction of computed tomography (CT) lung images plays a crucial role in the diagnosis and treatment planning of lung cancer patients. However, noise in CT images poses a significant challenge, hindering the precise interpretation of internal tissue structures. Low-dose CT, with reduced radiation risks, has gained popularity. Nonetheless, inherent noise compromises image quality, potentially impacting diagnostic performance. Denoising autoencoder and unsupervised deep learning algorithms offer a promising solution. A dataset of CT images from patients suspected of lung cancer was categorized into four disease groups to evaluate different autoencoder models. Results showed that designed autoencoders effectively reduced noise, enhancing overall image quality. The semi-supervised autoencoder exhibited superior performance, preserving fine details and enhancing diagnostic information. This research underscores autoencoder models' potential in improving lung cancer diagnosis accuracy by reconstructing CT lung images, emphasizing the importance of noise reduction techniques in enhancing image quality and diagnostic performance. تفاصيل المقالة