Automatic detection of lung nodules in computed tomography images using U-Net
1 Division of Graduate Studies and Research, National Technological Institute of Mexico/ I.T. Morelia, Morelia, México.
2 Department of Systems and Computing National Technological Institute of Mexico / I.T. Morelia, Morelia, México.
Research Article
Global Journal of Engineering and Technology Advances, 2024, 21(01), 105–114.
Article DOI: 10.30574/gjeta.2024.21.1.0191
Publication history:
Received on 31 August 2024; revised on 11 October 2024; accepted on 14 October 2024
Abstract:
Lung cancer is one of the leading causes of cancer-related deaths methods for lung nodules in computed tomography (CT) images rely on manual interpretation by radiologist, which can be time-consuming and prone to human error. This paper presents BreathSafe.AI a deep learning system for the automatic detection and segmentation of lung nodules in CT images using an enhanced U-Net architecture combined with dense network techniques. Our model is trained on the LUNA16 dataset, utilizing advanced image preprocessing and segmentation methods to optimize nodule detection. This system achieves a diagnostic accuracy of over 90%, significantly improving detection speed and consistency compared to existing methods. The results highlight the system’s potential to enhance lung cancer screening by reducing diagnosis time and variability, making it valuable tool for clinical use. Our approach demonstrates superior performance compared to state-of -art techniques, offering a scalable and efficient solution for early detection of lung cancer.
Keywords:
Lung cancer detection; Computed tomography; U-Net; Deep learning; Medical image segmentation
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0