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- W4312630774 abstract "Lung CT image segmentation plays an important role for providing an accurate lung CT image analysis. This paper introduces a new generalized framework for lung cancer segmentation. In this paper, we introduce a model which is utilized to segment CT scans using the U-Net algorithm, which plays an important role in image segmentation. The design of U-Net compromises of two paths; a contracting route for gathering all the knowledge and a symmetrical widening path to retrieve the required information. Additionally, data augmentation is undertaken to overcome the problem of lack of the available data and improve the accuracy. Cropping, resampling, zero centering, normalization, Gabor filter and edge detection using morphological algorithm are preprocessing steps, applied to improve our proposed algorithm performance. A K-fold cross validation is performed to evaluate our framework. Our proposed algorithm is performed on Lung Nodule Analysis 2016 (Luna 16) and National Lung Screening Trial (NLST) database. The proposed segmentation algorithm achieves 98.78% Jaccard Index and 98.75% dice-coefficient index. Moreover, the overall framework achieves an overall accuracy of 99.90%, 99.85% AUC, 99.09% sensitivity, 99.83% precision, 98.75% F1 score and computational time 2.2145 s." @default.
- W4312630774 created "2023-01-05" @default.
- W4312630774 creator A5010491131 @default.
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- W4312630774 date "2021-12-11" @default.
- W4312630774 modified "2023-10-06" @default.
- W4312630774 title "Lung CT Image Segmentation: A Generalized Framework Based on U-Net Architecture and Preprocessing Models" @default.
- W4312630774 doi "https://doi.org/10.1109/iccta54562.2021.9916619" @default.
- W4312630774 hasPublicationYear "2021" @default.
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