Brain Tumuor Detection.

This project aimed to detect brain tumors from MRI images using a TensorFlow-Keras implementation of the VGG19 architecture. Leveraging a dataset of 10,000 augmented MRI images, the model achieved an accuracy exceeding 80%. Through meticulous preprocessing, augmentation, and training, the VGG19-based model demonstrated robust performance in distinguishing between tumor and non-tumor regions in brain scans. With further refinement and potential deployment in clinical settings, this approach holds promise for enhancing the efficiency and accuracy of brain tumor diagnosis, showcasing the potential of deep learning in medical imaging tasks.