This model is designed for research and educational purposes in medical image analysis. It should NOT be used as the sole diagnostic tool in clinical settings.

Direct Use

python

import tensorflow as tf
from PIL import Image
import numpy as np

# Load model
model = tf.keras.models.load_model('model.keras')

# Prepare image
image = Image.open('your_image.jpg').resize((224, 224))
image_array = np.array(image) / 255.0
image_array = np.expand_dims(image_array, axis=0)

# Predict
predictions = model.predict(image_array)
class_names = ['Esophagitis', 'GERD', 'Normal', 'Ulcer']
predicted_class = class_names[np.argmax(predictions)]
confidence = np.max(predictions)

print(f"Predicted: {predicted_class} (Confidence: {confidence:.2%})")

Limitations and Bias

⚠️ Important Disclaimers:

This model is trained on a specific dataset and may not generalize to all populations
Medical imaging interpretation requires clinical expertise
Always consult healthcare professionals for medical decisions
The model may have biases based on the training data distribution

Training Data

The model was trained on an augmented GERD dataset containing gastroesophageal images across 4 categories.

Citation

If you use this model, please cite appropriately and acknowledge the original dataset sources.

Contact

For questions or issues, please open an issue in the model repository.

Developed for medical image classification research

ezAhmed/gerd-lightweight-vit

GERD Lightweight Vision Transformer 🔬

Model Details

Architecture

Training

Performance

Intended Use

Direct Use

Limitations and Bias

Training Data

Citation

Contact

ezAhmed/
gerd-lightweight-vit