| --- |
| 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) |
| |
