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Artificial Intelligence · Major Project
Alzheimer's Stage Classification from MRI
Classifies MRI slices into four dementia stages with a CNN
2-3 weeks build
Overview
Trains a CNN on a public four-class brain MRI dataset derived from OASIS, covering non-demented, very mild, mild and moderate dementia. The report compares a small custom network with transfer learning, discusses the risk of slice-level leakage, and a Streamlit app shows the predicted stage with a heatmap.
What makes it stand up
- 01Four-class MRI slice dataset derived from the public OASIS study
- 02Custom CNN versus pretrained backbone comparison
- 03Class weighting for the very small moderate-dementia class
- 04Per-class F1 and confusion matrix, with a leakage discussion
- 05Academic screening prototype, not a clinical diagnostic
Module breakdown
- 01
Dataset
Slice loading, resizing and stratified splits.
- 02
Baseline CNN
Compact convolutional network trained from scratch.
- 03
Transfer model
Pretrained backbone fine-tuned on MRI slices.
- 04
Explainability
Grad-CAM overlays on brain regions.
- 05
Streamlit app
Slice upload returning stage, confidence and heatmap.
After this, you will be able to
- Explain the difference between slice-level and subject-level evaluation
- Defend transfer learning on a small medical dataset
- Demo stage prediction on held-out slices
Technology stack
- Python
- TensorFlow
- Keras
- OpenCV
- Streamlit
- 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