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Artificial Intelligence · Major Project
Pneumonia Detection from Chest X-Rays
Screens chest X-rays for pneumonia with Grad-CAM explanations
2-3 weeks build
Overview
Fine-tunes a pretrained CNN on the public paediatric chest X-ray pneumonia dataset on Kaggle, re-splitting its tiny validation set into something usable. Every prediction comes with a Grad-CAM heatmap so examiners can see whether the model looks at the lungs or at labels and borders.
What makes it stand up
- 01Public Kaggle chest X-ray pneumonia dataset, properly re-split
- 02Transfer learning with class weighting for the normal minority
- 03Grad-CAM heatmaps to check the model attends to lung fields
- 04Sensitivity, specificity and ROC curves in the report
- 05Academic screening prototype, not a radiology diagnosis
Module breakdown
- 01
Dataset
Re-splitting, resizing and class-weight calculation.
- 02
Model
Pretrained backbone fine-tuned in two phases.
- 03
Explainability
Grad-CAM overlays on each chest X-ray.
- 04
Evaluation
Confusion matrix, ROC and threshold analysis.
- 05
Web app
Upload page returning label, confidence and heatmap.
After this, you will be able to
- Explain how Grad-CAM reveals shortcut learning
- Defend your re-split of the original validation set
- Demo predictions and heatmaps on held-out X-rays
Technology stack
- Python
- TensorFlow
- Keras
- OpenCV
- Flask
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 2-3 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support