Chiron

Source code for classification of brain tumors in MRI data using EfficientNetV2 architecture pretrained on ImageNet 1k dataset.

Getting started

Downloading the data

Download the dataset published by Cheng et al. consisting brains with three types of tumors: meningioma, glioma, and pituitary¹

(cd data/cheng-et-al && sh download.sh)

Download this Kaggle dataset consisting of brains without tumors

(cd data/kaggle && sh download.sh)

Preparing the data

Create and activate the conda environment

conda env create -f environment.yml && conda activate chiron

Open up Jupyter notebook

jupyter-notebook --notebook-dir notebooks

Run the notebooks prepare-cheng-et-al-data.ipynb and prepare-kaggle-data.ipynb to generate TFRecord files from the raw data. The notebook combined-data.ipynb can be used to combine the two datasets into a single dataset containing both positive and negative examples of brain tumors.

Footnotes

1. Cheng, Jun, et al. "Enhanced performance of brain tumor classification via tumor region augmentation and partition." PloS one 10.10 (2015): e0140381. ↩