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

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:

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.