SuryaThejas-07

SuryaThejas-07 / Early-Alzheimers-Detection

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NeuroScan MRI is an advanced deep learning system for early Alzheimer's detection using MRI scans. Built with PyTorch, it features a quantum attention-enhanced ResNet50 achieving 89.6% accuracy across four severity levels. The platform includes explainable AI (GradCAM visualization), an interactive Gradio web interface for diagnosis .

15
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69% credibility
Found Apr 22, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

A research tool that uses brain MRI scans to classify Alzheimer's disease stages with visualizations, reports, and an interactive web dashboard.

How It Works

1
🧠 Discover NeuroScan

You find this helpful tool online that analyzes brain scans to spot early signs of Alzheimer's disease.

2
⬇️ Get the files

You download everything needed and set it up on your computer so it's ready to use.

3
🚀 Start the dashboard

You open the simple web page that feels like a doctor's screening tool.

4
📁 Upload a scan

You drag in a brain MRI image, and it gets ready for analysis in seconds.

5
🎯 See the results

Watch as it quickly shows the likely stage of impairment with a confidence score and colorful risk gauge.

6
🔍 Dive into details

Explore easy-to-understand charts and heatmaps highlighting key areas in the scan.

📄 Get your report

Download a clear PDF summary with diagnosis, visuals, and advice, perfect for sharing with doctors.

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AI-Generated Review

What is Early-Alzheimers-Detection?

This Jupyter Notebook project, built with PyTorch, lets you detect early Alzheimer's across four severity levels from MRI scans using an advanced deep learning model achieving 89.6% accuracy. Upload an MRI image via the Gradio web interface, get instant diagnosis with confidence scores, GradCAM heatmaps for explainable AI, and exportable PDF/JSON/CSV reports. It solves the challenge of accessible early detection for Alzheimers by packaging pretrained models and a ready-to-run neuro MRI scan pipeline.

Why is it gaining traction?

The attention-enhanced architecture stands out with strong accuracy across imbalanced classes, plus user-facing perks like real-time predictions, test-time augmentation for robustness, and polished visualizations including risk gauges and confusion matrices. Developers appreciate the one-click Gradio launch for demos, GPU acceleration out of the box, and comprehensive reports that speed up prototyping without rebuilding from scratch. It's a practical hook for quick medical AI experiments versus barebones classifiers.

Who should use this?

Medical AI researchers validating early Alzheimer's detection models on standard MRI datasets. Clinicians or students prototyping diagnostic tools for research presentations. Devs in health tech building proof-of-concepts with explainable features and batch processing.

Verdict

Grab it for fast prototyping if you're in medical imaging—solid docs and pretrained models make it usable despite 15 stars and a 0.7% credibility score signaling early maturity. Test thoroughly on your data before production; lacks extensive validation or tests.

(198 words)

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