Ecg Classification Model

🚀 ECG Classification Model

This deep learning model is tailored for ECG image classification, leveraging a fine - tuned ResNet - 50 architecture. It excels at categorizing ECG images, offering valuable support in the detection of heart diseases.

🚀 Quick Start

Use the following code to load and use the model:

import tensorflow as tf
from PIL import Image
import numpy as np

# Load the model
model = tf.keras.models.load_model("https://huggingface.co/your-username/ecg_model/resolve/main/model.keras")

# Preprocess input image
def preprocess_image(image_path):
    img = Image.open(image_path).convert("RGB").resize((224, 224))
    img = np.array(img) / 255.0
    return np.expand_dims(img, axis=0)

# Make a prediction
image_path = "path/to/your/image.jpg"
input_image = preprocess_image(image_path)
prediction = model.predict(input_image)

print("Prediction:", prediction)

✨ Features

• ECG Image Classification: Capable of classifying ECG images into different categories related to heart disease conditions.
• Medical Assistance: Aids in medical research and preliminary diagnosis.
• Integration Potential: Can be integrated into larger healthcare applications for automated ECG analysis.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

The basic way to use this model is to load it and make predictions on ECG images as shown in the quick - start code above.

Advanced Usage

There are no advanced usage examples in the original document, so this part is not expanded.

📚 Documentation

Model Details

Model Description

Model Sources

• Repository: [Your Hugging Face Repo Link]
• Paper [optional]: [Link if available]
• Demo [optional]: [Link if available]

Uses

Direct Use

This model can be used to classify ECG images into different categories based on heart disease conditions. It can assist in medical research and preliminary diagnosis.

Downstream Use

This model can be integrated into larger healthcare applications for automated ECG analysis.

Out - of - Scope Use

• This model is not a replacement for professional medical diagnosis.
• Should not be used for self - diagnosis without expert consultation.

Bias, Risks, and Limitations

• Model accuracy depends on the diversity of training data.
• It may not generalize well to datasets from different sources.
• False positives/negatives could impact clinical decision - making.

Recommendations

Users should be made aware of the risks, biases, and limitations before using the model in real - world applications.

🔧 Technical Details

There are no specific technical details (more than 50 words) in the original document, so this section is skipped.

📄 License

The model is released under the Apache 2.0 license.