Brain Tumor Segmentation Pipeline Model Using U-Net Based Foundation Model
DOI:
https://doi.org/10.56294/dm2023197Keywords:
Segmentation, Forward Process, Reverse Process, Unconditional Image Generation, U-Net, Noise Schedulers, Positional EmbeddingAbstract
Medical professionals often rely on Magnetic Resonance Imaging (MRI) to obtain non-invasive medical images. One important use of this technology is brain tumor segmentation, where algorithms are used to identify tumors in MRI scans of the brain. The foundation model Pipeline is based on U-Net Architecture to handle medical image segmentation and has been fine-tuned in the research paper to segment brain tumors. The model will be further trained on various medical images to segment images for various bio-medical purposes and used as part of the Generative AI functional model framework. Accurate segmentation of tumors is essential for treatment planning and monitoring, and this approach can potentially improve patient outcomes and quality of life
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Copyright (c) 2023 Sanjeev Kumar Bhatt, Dr. S. Srinivasan, Piyush Prakash (Author)
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