LungRUNET: a Segmentation Framework for Lung Nodules

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 o...

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Bibliographic Details
Published inInternational 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
LanguageEnglish
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
Online AccessGet full text
ISSN2575-7288
DOI10.1109/ICACCS57279.2023.10113093

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Summary: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.
ISSN:2575-7288
DOI:10.1109/ICACCS57279.2023.10113093