Semantic Segmentation with Convolutional Neural Networks

Built and trained U‑Net, SegNet, ResNet, and ICNet models in TensorFlow for pixel‑wise segmentation, achieving 96.4 % accuracy on the Science Bowl nuclei dataset and 53.29 % mean IoU on Cityscapes (29 classes), establishing a high‑fidelity baseline for biomedical and urban imagery.