Brain Tumor Classification

Overview

Binary classification of brain X-ray images of patient diagnosed with brain tumor using deep neural networks.
Our results with MLP and Deep CNN models show that Deep CNN architectures are not always the solution to image classification.

Data

This project was carried out with data from Kaggle's Brain Tumor Dataset with train-test ratio of 8:2.

Brain Tumor

• X-ray images of brains with tumor
  

Healthy

• X-ray images of brains without tumor
  

Model

• Multi-Layered Perceptron(MLP)
• LeNet - Gradient-Based Learning Applied to Document Recognition, 1998, (Yann LeCun Leon Bottou Yoshua Bengio and Patrick Haffner)
• AlexNet - ImageNet Classification with Deep Convolutional Neural Networks, 2012, (Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton)
• ResNet16 - Deep Residual Learning for Image Recognition, 2015, (Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun)

All of the models were trained and tested via Pytorch framework
No pretrained model was used for this project

Results

Model	Accuracy	F1-Score
MLP	99.35%	0.99
LeNet	97.72%	0.98
AlexNet	94.02%	0.94
ResNet16	83.28%	0.83

Structure

Brain Tumor Classification
├── README.md
├── code
│   ├───AlexNet.ipynb
│   ├───LeNet.ipynb
│   ├───MLP.ipynb
│   └───ResNet.ipynb
│