README.md

metadata

tags:
  - image-classification
  - tensorflow
  - medical
  - mri
  - brain-tumor
datasets:
  - the-shoaib2/Brain_Tumor_MRI
library_name: tensorflow
pipeline_tag: image-classification
license: mit

MRI Brain Tumor Classification Model V3

This model is a fine-tuned Xception network for classifying brain MRI scans into 4 categories: glioma, meningioma, notumor, pituitary.

Model Details

• Architecture: Xception (Pre-trained on ImageNet)
• Input Size: (299, 299)
• Framework: TensorFlow / Keras
• Classes: 4 (glioma, meningioma, notumor, pituitary)

Training Configuration

• Epochs: 10
• Batch Size: 32
• Learning Rate: 0.001
• Optimizer: Adamax
• Loss: Categorical Crossentropy
• Metrics: Accuracy, Precision, Recall

Performance Metrics

Final Training Metrics (Epoch 10):
- Training Accuracy: 0.9979
- Training Loss: 0.0076
- Validation Accuracy: 0.9786
- Validation Loss: 0.1827
- Precision: 0.9979
- Recall: 0.9977

Training History

Metrics Plot

Final Metrics

Confusion Matrix

Data Distribution

Sample Predictions

Usage

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

# Load model
model = from_pretrained_keras("the-shoaib2/Brain_Tumor_MRI_Classification")

# Load and preprocess image
img = Image.open("path/to/mri_scan.jpg")
img = img.resize((299, 299))
img_array = np.array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0

# Predict
predictions = model.predict(img_array)
class_names = ['glioma', 'meningioma', 'notumor', 'pituitary']
predicted_class = class_names[np.argmax(predictions[0])]
confidence = np.max(predictions[0])

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

Model Architecture

The model uses transfer learning with Xception as the base:

• Xception base (pre-trained on ImageNet)
• Global Max Pooling
• Flatten layer
• Dropout (0.3)
• Dense layer (128 units, ReLU activation)
• Dropout (0.25)
• Output layer (4 units, Softmax activation)

Dataset

This model was trained on the Brain Tumor MRI Dataset.

Citation

If you use this model, please cite:

@misc{brain_tumor_mri_v3,
  author = {Shoaib},
  title = {MRI Brain Tumor Classification Model V3},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/the-shoaib2/Brain_Tumor_MRI_Classification}}
}