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 present in CT images poses a significant challenge, hindering the precise interpretation of internal tissue structures Low dose CT, with reduced radiation risks compared to conventional dose CT, has gained popularity Nonetheless, the noise inherent in these images compromises their quality, potentially impacting diagnostic performance Denoising autoencoder models, and unsupervised deep learning algorithms, offer a promising solution By reconstructing clean inputs from corrupted ones, the hidden layers of the autoencoder capture robust features In this study, a dataset of CT images from patients suspected of lung cancer was categorized into four disease groups, aiming to evaluate and compare different autoencoder models in terms of noise reduction and other evaluation criteria The results demonstrated that all the designed autoencoder models effectively reduced noise in CT images, improving overall image quality Notably, semi supervised autoencoder models exhibited superior performance, preserving fine details and enhancing diagnostic information This research highlights the potential of autoencoder models in improving the accuracy of lung cancer diagnosis by reconstructing CT lung images, emphasizing the importance of noise reduction techniques in enhancing image quality and diagnostic performance, with the semi supervised approach showing particular promise in preserving critical details
کلیدواژهها
نویسندگان
Mohammad Amin Pirian
Masters Student of Industrial Engineering, Tarbiat Modares University;
Iman Heidari
Masters Student of Industrial Engineering, Tarbiat Modares University;
Toktam Khatibi
Associate Professor at the Faculty of Industrial and Systems Engineering, Tarbiat Modares University;
Mohammad Mehdi Sepehri
Professor at the Faculty of Industrial and Systems Engineering, Tarbiat Modares University;
شیوه ارجاع
Pirian, Mohammad Amin and Heidari, Iman and Khatibi, Toktam and Sepehri, Mohammad Mehdi,1402,Enhancing Lung Cancer Diagnosis Accuracy through Autoencoder-Based Reconstruction of Computed Tomography (CT) Lung Images,The 9th International Conference on Industrial and Systems Engineering,Mashhad
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