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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

2. 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

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