U-Net based deep learning framework for automated lung nodule segmentation using CT images
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Abstract
Lung cancer is a leading cause of cancer-related fatalities globally. Early detection of lung nodules via CT scans is crucial for prompt diagnosis and treatment. This study presents a U-Net-based deep learning framework for automated lung nodule segmentation with the LIDC-IDRI dataset. The CT images and corresponding segmentation masks were preprocessed and used to train the proposed model. U-Net was adopted to identify the spatial features of lung nodules and to produce the masks at pixel level. The experimental results demonstrated learning performance with an accuracy of 99.44% and 99.46% during training and validation phases, respectively. The model yielded Dice Similarity Coefficient of 0.429 and localized the majority of nodule regions in CT images. Further visual comparison of the masks verified the validity of the proposed method. Thus, it can be concluded that the proposed system can aid in automatic localization of the lung nodules and in developing computer aided diagnosis systems.
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