|
--- |
|
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) |
|
|
