• Project
• Information
• Technologies
• Setup
• Poster
• Notebook
• Report
• Presentation
• Team

1. Data Exploration

• The Brain Tumor Detection 2020 Kaggle dataset, consisting of 3065 MRI images separated into two classes (Tumor – Non-Tumor) brain, was used.

2. Data Analysis

• cv2.COLOR_RGB2LAB was used to apply equalization histogram to perceptual lightness.
• Analysis of histograms indicated that the affected brains had much more perceptual lightness intensity.

3. CNN Model

• The model included mainly 3 Conv2D layers with maxpooling and ReLU activation functions, followed by Flatten, Dense, Activation, Dropout, Dense and Activation layers.

4. Model Training

• Binary crossentropy loss function was used for binary classification.
• Adam optimizer, a stochastic gradient descent optimization algorithm, was chosen to train the deep learning model as it can handle sparse gradients on noisy problems.

5. Model Evaluation

• The model achieved a validation accuracy of 97% which indicates its potential for detecting brain tumors accurately.

• This project is Mathematical Modeling using PDEs for Detection of Brain Tumor which was required by our college to further join a competition where our team pirates got the 2nd place in it. The content of this project includes:
  • Poster which illustrates our full project
  • Implementing our algorithm using Deep Learning to detect brain tumor in Jupyter Notebook (Accuracy 97%)
  • Report which describes the full research process
  • Presentation to present our full idea

• NumPy
• Matplotlib
• Keras
• TensorFlow
• PIL
• SciPy

To run this project, install it locally using pip:

Shell

$ cd ../"project_path"
$ jupyter notebook brain-tumor-detection-notebook.ipynb

Project Poster

Project Notebook

Project Report

Project Slides