ECG Classification Model

This deep learning model is designed for ECG image classification, fine-tuned using ResNet-50. It can classify ECG images into different categories to assist in heart disease detection.

Model Details

Model Description

Developed by: Adithian
Funded by: Adi
Shared by: Adi
Model type: Deep Learning (ResNet-based ECG Classification)
License: Apache 2.0
Finetuned from model: ResNet-50

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.

How to Use the Model

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)