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Brain Tumor Detection

This repository contains a deep learning project for detecting brain tumors using MRI images. The model is built using TensorFlow and Keras, and it classifies MRI images into two categories: 'yes' (tumor) and 'no' (no tumor).

Dataset

The dataset used in this project is from Kaggle: Brain MRI Images for Brain Tumor Detection.

Dependencies

  • Python 3.x
  • TensorFlow
  • Keras
  • OpenCV
  • NumPy
  • Matplotlib
  • scikit-learn

You can install the dependencies using the following command:

pip install tensorflow keras opencv-python numpy matplotlib scikit-learn

File Description

  • tumorrr.py: This is the main script that:
    • Loads and preprocesses the data.
    • Splits the data into training and testing sets.
    • Defines and trains the CNN model.
    • Evaluates the model and displays the results.

Model Architecture

The Convolutional Neural Network (CNN) model consists of:

  • Conv2D layers with ReLU activation
  • MaxPooling2D layers
  • Flatten layer
  • Dense layers with ReLU activation
  • Dropout layer
  • Output layer with Softmax activation

Usage

  1. Clone the repository:
git clone https://github.com/realmir1/BrainTumor.git
cd BrainTumor
  1. Run the script:
python tumorrr.py

Results

  • The model is trained for 10 epochs.
  • The test accuracy is displayed after training.
  • Sample predictions are visualized using Matplotlib.

Author

  • Ali Emir Sertbaş

Feel free to modify the README according to your specific needs.