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- W4383334351 abstract "The most precarious cancer in humans is lung cancer. With the problems arising in low accuracy and poor effect of lung nodule segmentation, U-Net-based semantic segmentation approaches are widely used. The paper aims to compare the different types of U-Net models, such as U-Net2D, R2U-Net2D, U-Net++, and Attention U-Net to get the best model out of these. The results from the experiments show that U-Net2D gave the best performance with an accuracy of 99.38%, 74.34% mean IOU, and 0.01 binary cross-entropy loss. Also, it is observed that the training and validation accuracy are approximately the same, thus showing no over-fitting problems, which can aid radiologists in detecting pulmonary lung nodules effectively." @default.
- W4383334351 created "2023-07-07" @default.
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- W4383334351 date "2023-04-21" @default.
- W4383334351 modified "2023-09-25" @default.
- W4383334351 title "Implementation of Different U-Net Architectures for Segmentation of Lung Cancer CT Images" @default.
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- W4383334351 doi "https://doi.org/10.1109/icaia57370.2023.10169245" @default.
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