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---
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)
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