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- W4384210080 abstract "Segmentation plays a significant role in the brain tumor analysis for its early diagnosis and treatment planning. Manual segmentation of brain tumors from MRI slices is a time-consuming procedure for cancer diagnosis. In subsequent therapy, brain tumor cells must divide automatically. Modern deep learning architectures utilize attention approaches for computer vision and nerve translation to increase networks’ spatial and channel-by-channel understanding. Existing approaches do not have powerful strategies for incorporating the details about information on tumor cells and their environment. In this study, we considered a deep learning model called “Attention based U-Net” based on boundary localization of brain tumor MRI scans. Models trained using U-Nets learn to putdown unimportant areas, while starring major features that terminates the Usage of explicit external organ/tissue localization modules. The segmented images may predict survival rate and treatment responsiveness in further process. Multimodal MRI scans were used to segment the glioma sub regions, which includes T1, T2, T1CE and FLAIR modalities. The proposed Attention U-Net model segmented the WT, TC and ET using the FLAIR modality and shown 95.56, 93.31 and 89.95 dice score respectively, over BraTS 2018." @default.
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- W4384210080 date "2023-01-01" @default.
- W4384210080 modified "2023-09-29" @default.
- W4384210080 title "Brain Tumor Segmentation Using 3D Attention U Net" @default.
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- W4384210080 doi "https://doi.org/10.1007/978-3-031-35641-4_39" @default.
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