A U-Net based deep learning model that automatically segments brain tumors in medical images. The system processes MRI scans to precisely identify and outline tumor regions, providing valuable diagnostic assistance for medical professionals. The model uses a custom loss function combining binary cross-entropy and Dice coefficient to achieve high accuracy in identifying tumor boundaries.
Automating the precise identification of brain tumor boundaries in medical images, reducing the time required for manual segmentation and potentially improving diagnostic accuracy.
The system implements a U-Net architecture with batch normalization for improved training stability. It employs a custom combined loss function that balances pixel-wise accuracy (binary cross-entropy) with structural similarity (Dice coefficient). The model demonstrates strong performance in accurately identifying tumor boundaries in previously unseen brain scans.
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