Deep Learning for Brain MRI Classification
Developed and implemented a CNN-based pipeline for brain MRI image classification as an academic project. Processed large sets of medical imaging data and applied preprocessing methods including intensity normalization and data augmentation to handle real-world variability. Built, trained, and evaluated deep learning models to identify Alzheimer’s and stroke conditions from medical images. • Used TensorFlow, Keras, and OpenCV for modeling and preprocessing • Focused on the domain of medical imaging analytics • Managed labeling tasks to create ground-truth datasets for model training and validation • Applied rigorous ML techniques to improve diagnosis accuracy