BRAIN TUMOR SEGMENTATION USING IMPROVED U-NET

Deep Learning

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is a vital task in medical image analysis. To develop an accurate and robust brain tumor segmentation model, this project takes advantage of deep learning and the Tenforflow and keras framework. The project encompasses a series of steps, from data acquisition to model deployment, aiming to assist healthcare professionals in the diagnosis and treatment of brain tumors. The project's foundation rests on acquiring a diverse dataset of brain MRI scans, which are carefully annotated with tumor masks. For model development, the architecture is implemented using tenforflow and keras, allowing us to leverage its powerful tensor operations and GPU support for accelerated training. To ensure the model's generalization to unseen data, rigorous validation is conducted on the validation dataset. Fine-tuning of hyper parameters is performed to enhance the model's performance, balancing factors like precision and recall. The model's ultimate evaluation takes place on the test dataset, where it is assessed for its real-world performance in tumor segmentation. The developed model, trained on carefully annotated MRI scans, promises to contribute significantly to brain tumor diagnosis and treatment.

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