Implement code to segment it, one-shot classify it, and segment+classify it with different models, then compare results.
Initial commit supports U-NET segmentation and adaptation to the unique image/mask file locations. Currently, only trains to segment normal images (no COVID), and does not do classificatin 48dcf26986b527868feac1f418e30ef933a2a055
Automatically downloads (assuming the user is logged into Kaggle with an account) and works with UNET segmentation as of: 01f09825c89f006aa8955784fe8be49e47fd4caf
A curious dataset of COVID lung X-Rays is available here: https://www.kaggle.com/datasets/anasmohammedtahir/covidqu
Implement code to segment it, one-shot classify it, and segment+classify it with different models, then compare results.
Initial commit supports U-NET segmentation and adaptation to the unique image/mask file locations. Currently, only trains to segment normal images (no COVID), and does not do classificatin 48dcf26986b527868feac1f418e30ef933a2a055