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