---
license: mit
language:
- en
pipeline_tag: image-classification
library_name: keras
---


# 🧠 Brain Tumor Multi-Class Classification (TensorFlow)

## Model Details

* **Model Name:** Brain Tumor Classification Model
* **Model Type:** Multi-class Image Classification
* **Framework:** TensorFlow / Keras
* **Architecture:** Convolutional Neural Network (CNN) *(or specify: e.g., EfficientNetB0, ResNet50, etc.)*
* **Task:** Classify brain MRI images into four categories:

  * No Tumor
  * Meningioma
  * Glioma
  * Pituitary Tumor

---

## Intended Use

### Primary Use Cases

* Assistive diagnostic tool for detecting brain tumor types from MRI scans
* Educational and research purposes
* Prototype for AI-powered medical imaging systems

### Out-of-Scope Use

* Not intended for real-world clinical diagnosis without expert validation
* Should not replace medical professionals

---

## Dataset

* **Type:** Brain MRI images
* **Classes:** 4 (No Tumor, Meningioma, Glioma, Pituitary)
* **Input Shape:** (e.g., 224 × 224 × 3)
* **Preprocessing:**

  * Resizing to fixed dimensions
  * Normalization (pixel values scaled to [0,1])
  * Data augmentation (rotation, flipping, zooming)

*(Add dataset source if public, e.g., Kaggle or hospital dataset)*

---

## Model Architecture

* Base model: *(e.g., EfficientNetB0 / Custom CNN)*

* Layers:

  * Convolutional layers for feature extraction
  * Pooling layers
  * Fully connected dense layers
  * Softmax output layer (4 neurons)

* **Loss Function:** Categorical Crossentropy

* **Optimizer:** Adam

* **Metrics:** Accuracy, Precision, Recall

---

## Training Details

* **Epochs:** (e.g., 20–50)
* **Batch Size:** (e.g., 16 or 32)
* **Train/Validation Split:** (e.g., 80/20)
* **Hardware:** CPU / GPU *(specify if available)*

---

## Evaluation Results

| Metric    | Value (example) |
| --------- | --------------- |
| Accuracy  | 92%             |
| Precision | 91%             |
| Recall    | 90%             |
| F1 Score  | 90%             |

### Confusion Matrix Insights

* Strong performance on **No Tumor** and **Pituitary**
* Some confusion between **Glioma** and **Meningioma** (common in MRI tasks)

---

## Limitations

* Performance depends heavily on dataset quality and diversity
* May not generalize well to:

  * Different MRI machines
  * Different populations
* Class imbalance can affect predictions
* Cannot explain predictions (unless paired with explainability tools like Grad-CAM)

---

## Ethical Considerations

* Risk of misclassification in sensitive medical contexts
* Must include human oversight in real applications
* Dataset bias could affect fairness across demographics

---

## How to Use

```python
import tensorflow as tf
import numpy as np
import cv2

model = tf.keras.models.load_model("model.h5")

img = cv2.imread("image.jpg")
img = cv2.resize(img, (224, 224))
img = img / 255.0
img = np.expand_dims(img, axis=0)

prediction = model.predict(img)
classes = ["No Tumor", "Meningioma", "Glioma", "Pituitary"]

print("Prediction:", classes[np.argmax(prediction)])
```

---

## Model Outputs

* **Input:** MRI brain image
* **Output:** Probability distribution across 4 classes

Example:

```
[0.85, 0.05, 0.07, 0.03]
→ No Tumor
```

---

## Future Improvements

* Use transfer learning (EfficientNet, Vision Transformers)
* Add explainability (Grad-CAM heatmaps)
* Deploy with optimized inference (TensorFlow Lite)
* Improve dataset diversity and size

---

## License

* Specify license (e.g., MIT, Apache 2.0)

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## Contact

* Name: *(Your Name)*
* Project: *(e.g., NeuroScopeAI)*
* Email/GitHub: *(optional)*

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If you want, I can **upgrade this into a Hugging Face model card format (README.md)** so you can upload it directly with your model.