Enhancing Lung Cancer Diagnosis Accuracy through Autoencoder-Based Reconstruction of Computed Tomography (CT) Lung Images
محورهای موضوعی : نشریه بینالمللی هوش تصمیمMohammad Amin Pirian 1 , Iman Heidari 2 , Toktam Khatibi 3 , Mohammad Mehdi Sepehri 4
1 - Systems and Industrial Engineering Department, Tarbiat Modares University, Tehran, Iran
2 - Industrial Engineering, Tarbiat Modares University, Tehran, Iran
3 - Associate Professor, School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
4 - Systems and Industrial Engineering Department, Tarbiat Modares University, Tehran, Iran
کلید واژه: Deep Learning, Autoencoder, Computed tomography image reconstruction, Image quality enhancement,
چکیده مقاله :
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.
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.
