Brain-Tumor-Classification

Table of Contents

1. Overview
2. Getting Started
   1. Dependencies
   2. Installation
3. Project Motivation
4. Results
5. Author
6. Licensing
7. Acknowledgements

Overview

Brain tumors account for 85% to 90% of all primary central nervous system tumors around the world, with the highest incidence and mortality belonging to high HDI regions. With some image classification techniques, I was able to train a model which could then not only determine the presence of a tumor from Brain MRI Scan but also classify the tumor into one of the following types: Glioma, Meningioma, Pituitary Tumor.

Getting Started

Dependencies

• Python 3.*
• Libraries: NumPy, Pandas, Seaborn, Matplotlib, cv2, Keras, tqdm
• Google Colaboratory

Installation

• Datasets: The complete set of files is publicly available and can be downloaded from Kaggle. Alternatively, you can find the folder (titled Brain-MRI) in my Github repository here.
• Others: The code can be run in Google Colab as an Interactive Python Notebook (ipynb). No additional installation is required.
  • Colaboratory allows you to use and share Jupyter notebooks with others without having to download, install, or run anything on your own computer (other than a browser).

Project Motivation

The Project builds a model that is trained on images of Brain MRI Scans, which it then uses to classify a test image into one of the following four categories :

• Glioma,
• Meningioma,
• Pituitary Tumor, or
• No Tumor

Gliomas: These are the tumors that occur in the brain and/or spinal cord. Types of glioma include: Astrocytomas, Ependymomas, and Oligodendrogliomas. Gliomas are one of the most common types of primary brain tumors. Meningiomas: These are the tumors that arise from the Meninges — the membranes that surround the brain and spinal cord. Most meningiomas grow very slowly, often over many years without causing symptoms. Pituitary tumors: These are the tumors that form in the Pituitary — a small gland inside the skull. Most pituitary tumors are often pituitary adenomas, benign growths that do not spread beyond the skull.

I cropped and augmented the images before building, compiling, training, and evaluating the model.

Results

In the end, I could validate a test image passed through the model.

Author

• Nazia N.

Licensing

• The dataset is available under the Open Database License ODbL.
• Any rights in individual contents of the database are licensed under the Database Contents License.

Acknowledgements

Thanks to Ashish for his guidance throughout the project.