Standard retinal vessel segmentation models produce fragmented, disconnected vessels, limiting reliable clinical analysis. Built a topology-aware model that preserves vascular connectivity using graph-based feature fusion and topology-driven loss functions—achieving SOTA performance while reducing vessel fragmentation by ~38%.

Skin cancer classification models struggle with diverse lesion types, image artifacts, and dataset imbalance, limiting diagnostic reliability. Built an attention-enhanced ensemble combining ResNet50V2, MobileNetV2, and EfficientNetV2, along with robust preprocessing for artifact removal—achieving superior precision/recall and strong performance across challenging lesion classes.