|
|
---
|
|
|
language: en
|
|
|
license: mit
|
|
|
tags:
|
|
|
- tensorflow
|
|
|
- image-classification
|
|
|
- medical-imaging
|
|
|
- vascular-dementia
|
|
|
datasets:
|
|
|
- custom
|
|
|
---
|
|
|
|
|
|
# EfficientNet Model for Vascular Dementia Detection
|
|
|
|
|
|
This model was trained to detect Vascular Dementia (VAD) from MRI scans. It uses an EfficientNet architecture fine-tuned on a custom dataset of brain MRI images.
|
|
|
|
|
|
## Model Description
|
|
|
|
|
|
- **Model Type:** EfficientNet
|
|
|
- **Task:** Binary classification (VAD-Demented vs. Non-Demented)
|
|
|
- **Input:** MRI brain scans (224x224 RGB)
|
|
|
- **Output:** Binary classification with confidence score
|
|
|
|
|
|
## Usage
|
|
|
|
|
|
```python
|
|
|
import tensorflow as tf
|
|
|
import numpy as np
|
|
|
from PIL import Image
|
|
|
|
|
|
# Load the model
|
|
|
model = tf.keras.models.load_model("path/to/downloaded/model")
|
|
|
|
|
|
# Preprocess your image
|
|
|
image = Image.open("path/to/your/mri.jpg")
|
|
|
image = image.resize((224, 224))
|
|
|
image_array = np.array(image) / 255.0
|
|
|
image_array = np.expand_dims(image_array, axis=0)
|
|
|
|
|
|
# Get prediction
|
|
|
prediction = model.predict(image_array)
|
|
|
predicted_class = "VAD-Demented" if prediction[0][0] > 0.5 else "Non-Demented"
|
|
|
confidence = prediction[0][0] * 100 if prediction[0][0] > 0.5 else (1 - prediction[0][0]) * 100
|
|
|
|
|
|
print(f"Prediction: {predicted_class}")
|
|
|
print(f"Confidence: {confidence:.2f}%")
|
|
|
```
|
|
|
|
|
|
## API Usage
|
|
|
|
|
|
This model can be used directly with the Hugging Face Inference API:
|
|
|
|
|
|
```python
|
|
|
import requests
|
|
|
import base64
|
|
|
from PIL import Image
|
|
|
import io
|
|
|
|
|
|
# Convert image to base64
|
|
|
image = Image.open("path/to/your/mri.jpg")
|
|
|
buffered = io.BytesIO()
|
|
|
image.save(buffered, format="JPEG")
|
|
|
img_str = base64.b64encode(buffered.getvalue()).decode()
|
|
|
|
|
|
# API endpoint
|
|
|
API_URL = "https://api-inference.huggingface.co/models/thakshana02/vad-efficientnet-model"
|
|
|
|
|
|
# API headers with your token
|
|
|
headers = {"Authorization": "Bearer YOUR_TOKEN"}
|
|
|
|
|
|
# Make prediction request
|
|
|
response = requests.post(API_URL, headers=headers, json={"inputs": {"image": img_str}})
|
|
|
result = response.json()
|
|
|
print(result)
|
|
|
```
|
|
|
|
|
|
## Limitations
|
|
|
|
|
|
This model is intended for research purposes only and should not be used for clinical diagnosis without proper validation by healthcare professionals.
|
|
|
|
