U-DENSENET Deep Learning Model for Medical Image Segmentation
DOI:
https://doi.org/10.21015/vtcs.v12i2.1922Abstract
Medical image segmentation, particularly in brain MRIs, is critical for identifying neurological disorders and planning treatment. This paper presents a novel deep learning U-DenseNet model that combines the strengths of DenseNet and UNet architectures for improved brain tumor segmentation and skull-stripped segmentation. Evaluated on both skull-stripped and brain tumor datasets, the U-DenseNet model achieves a Dice coefficient of 0.9125 and accuracy of 99.81\% for brain tumor segmentation and a Dice coefficient of 0.9902 and accuracy of 98.49\% for skull-stripped segmentation. The architecture of U-DenseNet model effectively captures fine anatomical details while ensuring computational efficiency, making it suitable for clinical applications.
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