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- W3204145332 abstract "Manual assessment of parotid gland status from magnetic resonance imaging is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to get consistent, objective and rapid diagnoses of parotid gland tumor. In this kind of cancer, a large variety in their characteristics, that brings various difficulties for traditional image analysis methods. In this paper, we propose an automatic method to perform both segmentation and classification of Parotid gland region in order to give quantitative assessment and uniform indicators of PGs status that will help pathologists in their diagnostic. The experimental result illustrates the high accuracy of the results of the proposed method compared to the ground truth. Furthermore, a comparative study with existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method." @default.
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- W3204145332 date "2021-08-31" @default.
- W3204145332 modified "2023-10-10" @default.
- W3204145332 title "Improving Parotid Gland Tumor Segmentation and Classification Using Geometric Active Contour Model and Deep Neural Network Framework" @default.
- W3204145332 doi "https://doi.org/10.18280/ts.380405" @default.
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