The challenge
Reading medical scans is slow, specialist-dependent work.
The goal was screening models that flag likely brain tumors and diabetic retinopathy, helping clinicians prioritize which cases to review first.
- 01
ScanMedical image intake
- 02
PrepareImage preprocessing
- 03
BackbonePretrained CNN (transfer learning)
- 04
ClassifyTumor and retinopathy heads
- 05
EvaluateHeld-out validation
Architecture · 5 stages
- 01
Scan
Medical image intake
- 02
Prepare
Image preprocessing
- 03
Backbone
Pretrained CNN (transfer learning)
- 04
Classify
Tumor and retinopathy heads
- 05
Evaluate
Held-out validation
The solution
I built and trained convolutional neural networks in TensorFlow, using transfer learning from pretrained backbones to get strong performance from limited labeled medical data.
The pipeline covers image preprocessing, training and evaluation for both tasks, and is designed as clinical decision support rather than diagnosis.
Engineering highlights
- Brain-tumor detection classifier
- Diabetic-retinopathy screening classifier
- Transfer learning for limited labeled data
- Designed as decision support for clinicians
Technology
- Python
- TensorFlow
- Keras
- CNNs
- Transfer learning
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