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---
license: apache-2.0
---
# 🧠 Brain MRI Classification with TensorFlow and Streamlit
## 🇬🇧 English
This project is a deep learning-based solution for classifying Brain MRI images into four categories: **Glioma**, **Meningioma**, **Pituitary**, and **Normal**. The classification model is trained using TensorFlow/Keras and can be used either programmatically or with a Streamlit web interface.
### 🧪 Model Information
- Input shape: `(224, 224, 3)`
- Model format: `.keras` (SavedModel)
- Output classes:
- 0: Glioma
- 1: Meningioma
- 2: Normal
- 3: Pituitary
---
### 🧠 Example Usage (Python Script)
```python
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
# Load the trained model
model = load_model('brainmri.keras')
# Load and preprocess the image
img = image.load_img('example.jpeg', target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Define class labels
classes = {
0: "Glioma",
1: "Meningioma",
2: "Normal",
3: "Pituitary"
}
# Make prediction
result = model.predict(img_array)
predicted_class = np.argmax(result)
# Print result
print("Predicted Class:", classes.get(predicted_class))
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