HealthAI Chest X-Ray Classification
A chest X-ray classification workflow that demonstrates image preprocessing, model training, classification metrics, confusion matrix analysis, and careful discussion of model limitations.
Project Summary
- Status
- Completed
- Timeline
- Completed project
- Visual proof
- Image -> Model -> Evaluation
Placeholder visual
Featured case-study visual
Placeholder visual: X-ray sample, prediction card, and confusion matrix.
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Problem
Medical image models require careful evaluation because accuracy alone can hide clinically important false negatives and false positives.
Approach
Train and evaluate an image classification pipeline, then present model behavior through metrics, confusion matrix analysis, and error examples.
Architecture
System flow and processing stages.
Stage 1
Chest X-ray dataset
Stage 2
Image preprocessing
Stage 3
Train/validation split
Stage 4
Model training
Stage 5
Prediction
Stage 6
Evaluation metrics
Stage 7
Error analysis and model card
Data Sources
- Public chest X-ray dataset
- Labeled medical image samples
Methods
- Image preprocessing
- Classification
- Model evaluation
- Confusion matrix analysis
- Responsible AI framing
Technologies
- Python
- PyTorch or TensorFlow
- scikit-learn
- OpenCV
- matplotlib
- seaborn
Evidence and Screenshots
Visual assets to replace placeholders.
Placeholder visual
Confusion matrix
Create this screenshot and replace this placeholder in the project assets.
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Placeholder visual
Prediction interface with sample X-ray and confidence
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Placeholder visual
False positive/false negative gallery
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Results
- Built a chest X-ray classification workflow.
- Evaluated model behavior with classification metrics and confusion matrix analysis.
- Documented limitations and risks of medical AI predictions.
Metrics and Evaluation Needed
- Precision/recall/F1 table
- Confusion matrix
- ROC or PR curve
- Class distribution chart
Challenges
- Medical datasets can have imbalance and bias.
- False negatives and false positives have different risk implications.
- A portfolio demo must avoid overstating clinical usefulness.
Lessons Learned
- Healthcare AI needs evaluation beyond accuracy.
- Error analysis and limitations are essential to responsible ML communication.
- Prediction confidence should be shown with careful disclaimers.
Future Work
- Add Grad-CAM visualizations.
- Add a full model card.
- Add external validation or stronger baseline comparisons.
Interactive Demo Ideas
- Precomputed prediction explorer for sample X-rays
- Metric tabs for evaluation visuals
- Model card section
What This Demonstrates