LungRUNET: a Segmentation Framework for Lung Nodules

• Published in: International Conference on Advanced Computing and Communication Systems (Online) Vol. 1; pp. 658 - 661
• Main Authors: Bruntha, P. Malin, Dhanasekar, S., Ahmed, L. Jubair, Govindaraj, V., Pandian, S. Immanuel Alex, Abraham, Siril Sam
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.03.2023
• Subjects: Communication systems; Computational modeling; Computed tomography; Image analysis; Image segmentation; Lung; Lung Cancer; Lung Nodule; Nodule Segmentation; Pulmonary Nodule; Sensitivity; UNet
• ISSN: 2575-7288
• DOI: 10.1109/ICACCS57279.2023.10113093

In the modern-day world, deaths due to cancer are on the increase alarmingly. Among various types of cancer, lung cancer is very deadly. If detected early, however, the mortality rates due to lung cancer can be brought down drastically. Lung nodule segmentation is now possible using image analysis of Computed Tomography images of lung thoracic regions. The present study suggests a new model namely Lung Residual UNet (Lung_RUNET) based on UNet architecture for the effective segmentation of lung nodules. The proposed model was checked on the publicly available LIDC-IDRI dataset. The results were compared with the UNet model and have shown significant improvement in segmentation with 87.2% in DSC, 81.1% in sensitivity and 89.1% in precision.