---
language:
  - en
license: apache-2.0
metrics:
  - accuracy
base_model: resnet50
pipeline_tag: image-classification
library_name: keras
tags:
  - medical
  - ecg
  - classification
  - deep-learning
  - healthcare
  - tensorflow
  - keras
---
# 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:

```python
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)