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- W3171995517 abstract "Lung cancer is one of the types of cancer that claims the most lives globally. For screening purposes, computed tomography scans are the most reliable source for nodule detection, as it reveals the structure of the chest, through a three dimensional representation, in which lung lesions can be fully observed. For early cancer detection, it is necessary to use computed radiography and tomography of the thorax, as well as searches for potentially malignant nodules by specialists. In this paper, lung nodule segmentation was performed using the LIDC IDRI public database, which includes images of computed tomographies, by means of a modified U-Net convolutional neural network. The experimental results have shown that our proposal achieves a Dice similarity coefficient of 88.1% and accuracy of 99.78%, which improves nodule segmentation performance in comparison with other architectures used in the literature.KeywordsLung cancerImage segmentationConvolutional neural networkU-NetComputed tomography" @default.
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- W3171995517 date "2021-01-01" @default.
- W3171995517 modified "2023-09-26" @default.
- W3171995517 title "Lung-Nodule Segmentation Using a Convolutional Neural Network with the U-Net Architecture" @default.
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- W3171995517 doi "https://doi.org/10.1007/978-3-030-77004-4_32" @default.
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