amulmm/brain_tumor_training

Brain Tumor Detection Model

This repository contains a deep learning model for brain tumor detection from MRI images. The model is built using TensorFlow/Keras and is designed to classify MRI scans into different categories: glioma, meningioma, notumor, pituitary, and non_brain.

Model Details

• Model Type: Keras H5 model
• Model File: brain_tumor_model_baru.h5
• Input Image Size: 150x150 pixels
• Classes: ['glioma', 'meningioma', 'notumor', 'pituitary', 'non_brain']

How to Use (Local Inference)

1. Clone the repository:

   git clone https://huggingface.co/amulmm/brain_tumor_training # Replace with your actual repo URL
   cd brain_tumor_training
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Flask application (if applicable, based on app.py):

   python app.py
   

   Then navigate to http://127.0.0.1:5000/ in your web browser.

Hugging Face Inference API

You can use the Hugging Face Inference API to get predictions from this model. Here's an example Python script:

import requests

API_URL = "https://api-inference.huggingface.co/models/amulmm/brain_tumor_training" # Replace with your actual repo ID
headers = {"Authorization": "Bearer YOUR_HUGGING_FACE_API_TOKEN"}

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

# Example usage:
# output = query("path/to/your/image.jpg")
# print(output)

Replace YOUR_HUGGING_FACE_API_TOKEN with your actual Hugging Face API token and path/to/your/image.jpg with the path to your MRI image file.

Training Data

The model was trained on a dataset of brain MRI images. (Add more details about your dataset if available, e.g., source, size, distribution of classes).

License

(Specify your model's license here, e.g., MIT, Apache 2.0, etc.)