metadata
license: mit
datasets:
- brain-mri-dataset
metrics:
- accuracy
- auc
model-index:
- name: CNN Brain Tumor Classifier
results:
- task:
type: image-classification
dataset:
name: Brain MRI Dataset
type: brain-mri-dataset
metrics:
- name: Accuracy
type: accuracy
value: 0.9
- name: AUC
type: auc
value: 0.9
🧠 CNN Brain Tumor Classifier
Model Description
This repository contains a Convolutional Neural Network (CNN) built with TensorFlow/Keras for classifying brain MRI scans.
The model can distinguish between three types of brain tumors and healthy scans.
⚠️ Disclaimer: This model is provided for educational and research purposes only.
It is not a medical diagnostic tool and should not be used in clinical practice.
Classes
The model predicts one of the following four categories:
- Glioma
- Meningioma
- Pituitary tumor
- No tumor (healthy)
Training Details
- Framework: TensorFlow / Keras
- Architecture: Custom CNN
- Input size: 224 × 224 RGB MRI images
- Loss function:
categorical_crossentropy - Optimizer:
Adam - Epochs: 10
- Metrics: Accuracy, AUC
Dataset
- Source: Public brain MRI dataset (glioma, meningioma, pituitary, no tumor)
- Preprocessing:
- Images resized to 224 × 224
- Normalized to [0, 1] range
- Augmentation (rotation, flipping, zoom) applied during training
Evaluation Results
| Metric | Value |
|---|---|
| Training Accuracy | ~96% |
| Validation Accuracy | ~85–90% |
| AUC (training) | ~0.90 |
(values may vary depending on train/validation split)
Usage
Installation
pip install tensorflow huggingface_hub
from tensorflow.keras.models import load_model
from huggingface_hub import hf_hub_download
import numpy as np
from tensorflow.keras.preprocessing import image
# Download model file from Hugging Face Hub
model_path = hf_hub_download(
repo_id="larrikin-coder/brain-tumor-cnn", # replace with your repo
filename="cnn_model.h5"
)
# Load model
model = load_model(model_path)
# Preprocess an image
img = image.load_img("test_mri.jpg", target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Predict
pred = model.predict(img_array)
class_names = ["glioma", "meningioma", "pituitary", "no_tumor"]
print("Prediction:", class_names[np.argmax(pred)])
@misc{larrikin-coder2025, title={CNN Brain Tumor Classifier}, author={Larrikin Coder}, year={2025}, howpublished={\url{https://huggingface.co/larrikin-coder/brain-tumor-cnn}} }