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- W2901188035 abstract "Lung Cancer is the most perilous cancer. Early detection of the disease can improve survival rate. Automation of detection of lung nodules aid radiologists in quickly and accurately diagnosing the disease. Developing computer aided diagnosis (CADx) systems for lung cancer is a challenging task. Several components make up CADx and one of the most significant components is lung segmentation. Segmentation of lungs is an essential prerequisite to efficiently detect and classify lung nodules. Lung segmentation is the process of segregating lungs region from other tissues in the CT image. Conventional methods for lung segmentation either do not accurately segments normal and abnormal lungs or rely heavily on user generated features for the lungs. Deep learning has outperformed other methods in image processing and computer vision tasks. An architecture called U-Net convolutional network has been proposed and implemented exclusively for the segmentation of biomedical images. In this study U-Net ConvNet has been implemented on lungs dataset to perform lungs segmentation. The lungs dataset consists of 267 CT images of lungs and their corresponding segmentation maps. The accuracy and loss achieved is 0.9678 and 0.0871 respectively. Hence U-Net ConvNet can be used for the segmentation of lungs in CT scans." @default.
- W2901188035 created "2018-11-29" @default.
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- W2901188035 date "2018-04-01" @default.
- W2901188035 modified "2023-10-18" @default.
- W2901188035 title "Automatic Lung Segmentation on Thoracic CT Scans Using U-Net Convolutional Network" @default.
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- W2901188035 doi "https://doi.org/10.1109/iccsp.2018.8524484" @default.
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