adi9-48
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ecg_classification_model

@@ -1,205 +1,88 @@
 ---
 language:
-- en
-- ml
-- hi
-- ja
-- fr
 license: apache-2.0
 metrics:
-- accuracy
 ---
-# Model Card for Model ID
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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)