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Latest Work
SkinIQ — A Dermatologist in Your Pocket
From model training to production — end-to-end ML deployment.
Fine-tuned EfficientNet-B3 on HAM10000 (10k dermoscopy images, 7 skin condition classes) with weighted loss and WeightedRandomSampler to handle a 58:1 class imbalance. Deployed a Dockerized FastAPI inference server on Railway — model weights auto-downloaded from HuggingFace Hub on startup. React/Vite frontend on Vercel with Supabase for auth, Row Level Security, and scan history. Full product: scan, track, and visualize your skin health.
POST /predict
image: base64_encoded_photo
# EfficientNet-B3 inference
# weights: HuggingFace Hub
# deploy: Railway + Docker
{
"disease": "Melanocytic Nevus",
"clinicalUrgency": "Low",
"confidence": 0.847
}