Alzheimer MRI ConvNeXt Classifier

This repository contains a GPU-accelerated deep learning model for classifying Alzheimer’s disease stages from brain MRI images using a ConvNeXt-based architecture.

The model is designed for research, educational use, and technical demonstrations, and is deployed as a Hugging Face Inference Endpoint for fast GPU inference.

🧠 Model Overview

Task: MRI image classification
Modality: Brain MRI (2D slices)
Architecture: ConvNeXt
Framework: PyTorch
Deployment: Hugging Face GPU Inference Endpoint

The model predicts probabilities over predefined Alzheimer-related classes provided in class_names.json.

📦 Repository Structure

├── inference.py # Hugging Face inference entrypoint ├── requirements.txt # Minimal runtime dependencies ├── models/ │ ├── best_model.pth # Trained ConvNeXt weights │ └── class_names.json # Class index → label mapping └── README.md

⚡ Inference

The model is exposed via a Hugging Face Inference Endpoint and accepts an image file as input.

Example API Call

import requests

API_URL = "https://<your-endpoint>.endpoints.huggingface.cloud"
HEADERS = {
    "Authorization": "Bearer YOUR_HF_TOKEN"
}

with open("sample_mri.png", "rb") as f:
    response = requests.post(
        API_URL,
        headers=HEADERS,
        files={"file": f}
    )

print(response.json())

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