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KaiSKX/
Alzheimer_ConvNeXtCNN

Name: Alzheimer_ConvNeXtCNN
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ConvNeXt CNN classification for Alzheimer disease 🧠

PyTorch

timm

ONNX

MLflow

Databricks

ConvNeXt-based CNN for Alzheimer's disease classification from MRI images

🎯 Model Overview

A pre-trained ConvNeXt CNN model is fine-tuned for classifying Alzheimer's disease stages from brain MRI scans. The model distinguishes between 4 classes:

Classes:

Non-Demented
Very Mild Demented
Mild Demented
Moderate Demented

🏗️ Architecture & Approach

Backbone Model: ConvNeXt (Meta AI, 2022) - A modern SOTA CNN architecture inspired by Vision Transformers but built purely on convolutions.

Transfer Learning Strategy: Progressive unfreezing approach with ImageNet-pretrained weights:

Step 1: Train classification head only (10 epochs)
                        ↓
Step 2: Unfreeze stage 3 + head (5 epochs, reduced LR)
                        ↓
Step 3: Unfreeze stage 2 + stage 3 + head (5 epochs, further reduced LR)

📊 Dataset & Performance

Sample of Alzheimer's MRI dataset used for training

⚡ Confusion Matrix & Accuracy

Confusion Matrix for Alzheimer classification

Test Accuracy: 98.78% across 4 classes.

⚡ Inference Speed Benchmark (CPU)

Format	Inference Time	Relative Speed
PyTorch (.pth)	148.16 ms/image	Baseline
ONNX (.onnx)	132.48 ms/image	1.12x faster

NOTE: The result is based on Kaggle notebook CPU environment. Different environment will have different effect but normally ONNX runtime engine has a better inference speed.

🚀 Quick Start

Download Models

bash

from huggingface_hub import snapshot_download

# Download entire model directory (MLflow Model)
model_dir = snapshot_download(repo_id="KaiSKX/Alzheimer_ConvNeXtCNN", repo_type="model")

# or download specific format (.onnx)
from huggingface_hub import hf_hub_download
onnx_path = hf_hub_download(repo_id="KaiSKX/Alzheimer_ConvNeXtCNN", filename="onnx/convnext_model.onnx", repo_type="model")

# or download specific format (.pth)
from huggingface_hub import hf_hub_download
onnx_path = hf_hub_download(repo_id="KaiSKX/Alzheimer_ConvNeXtCNN", filename="data/model.pth", repo_type="model")

💻 Usage

MLflow Model (PyTorch flavor)

Best for: MLflow/Databricks deployment or inference

python

import mlflow.pytorch
import torch
from PIL import Image
import torchvision.transforms as transforms
import time

# Load MLflow model
model = mlflow.pytorch.load_model("Alzheimer_ConvNeXtCNN")
model.eval()

# Define class names for mapping
class_names = ["Mild Demented", "Moderate Demented", "Non Demented", "Very Mild Demented"]

# Prepare input
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

image = Image.open("C:/Users/dream/Downloads/test_dataset/ModerateDemented/moderateDem39.jpg").convert("RGB")
input_data = transform(image).unsqueeze(0)

# Inference with timing [optional for time]
with torch.no_grad():
    start_time = time.time()
    output = model(input_data)
    end_time = time.time()

predicted_class = torch.argmax(output, dim=1).item()
inference_time = (end_time - start_time) * 1000

print(f"Predicted class: {class_names[predicted_class]}")
print(f"Inference time: {inference_time:.2f} ms")

ONNX Runtime

Best for: Cross-platform compatibility and faster CPU inference

python

import onnxruntime as ort
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
import time

# Load ONNX model
session = ort.InferenceSession("onnx/convnext_model.onnx")
input_name = session.get_inputs()[0].name

# Define class names for mapping
class_names = ["Mild Demented", "Moderate Demented", "Non Demented", "Very Mild Demented"]

# Prepare input
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

image = Image.open("C:/Users/dream/Downloads/test_dataset/ModerateDemented/moderateDem39.jpg").convert("RGB")
input_data = transform(image).unsqueeze(0).numpy().astype(np.float32)

# Inference with timing [optional for time]
start_time = time.time()
output = session.run(None, {input_name: input_data})[0]
end_time = time.time()

inference_time = (end_time - start_time) * 1000
predicted_class = np.argmax(output, axis=1)[0]

print(f"Predicted class: {class_names[predicted_class]}")
print(f"Inference time: {inference_time:.2f} ms")

🔧 Training Details

Dataset: Public Alzheimer's MRI dataset (see Kaggle Dataset)
Pretraining: ImageNet-pretrained ConvNeXt weights via timm
Multi-GPU: Parallel training acceleration via NVIDIA T4 * 2
MLOps: Databricks MLflow integration for experiment tracking and model registry

⚠️ Important Notice

Research & Portfolio Use Only: This model is developed for educational and portfolio demonstration purposes. It is NOT intended for professional clinical diagnosis, medical decision-making, or any real-world medical applications**.

For detailed training methodology and MLOps implementation, please refer to the Kaggle notebook.