/Breast-Cancer-Detection-Code

Common deep learning pipeline for the Breast Cancer Detection Dissertation

Primary LanguagePythonBSD 2-Clause "Simplified" LicenseBSD-2-Clause

Breast Cancer Detection in Mammograms using Deep Learning Techniques - Common Pipeline Code DOI GitHub license

Repository containing the code written in common for the Breast Cancer Detection in Mammograms using Deep Learning Techniques dissertation. This code was further extended individually by each group member to get results by testing new deep learning techniques.

Usage on a GPU lab machine

Clone the repository:

cd ~/Projects
git clone https://github.com/Adamouization/Breast-Cancer-Detection-Code

Create a repository that will be used to install Tensorflow 2 with CUDA 10 for Python and activate the virtual environment for GPU usage:

cd libraries/tf2
tar xvzf tensorflow2-cuda-10-1-e5bd53b3b5e6.tar.gz
sh build.sh

Activate the virtual environment:

source /cs/scratch/<username>/tf2/venv/bin/activate

Create outputand save_models directories to store the results:

mkdir output
mkdir saved_models

cd into the src directory and run the code:

python main.py [-h] -d DATASET -m MODEL [-r RUNMODE] [-i IMAGESIZE] [-v]

where:

  • -h is a flag for help on how to run the code.
  • DATASET is the dataset to use. Must be either mini-MIAS or CBIS-DDMS.
  • MODEL is the model to use. Must be either basic or advanced.
  • RUNMODE is the mode to run in (train or test). Default value is train.
  • IMAGESIZE is the image size to feed into the CNN model (small - 512x512px; or large - 2048x2048px). Default value is small.
  • -v is a flag controlling verbose mode, which prints additional statements for debugging purposes.

Dataset usage

mini-MIAS dataset

  • This example will use the mini-MIAS dataset. After cloning the project, travel to the data/mini-MIAS directory (there should be 3 files in it).

  • Create images_original and images_processed directories in this directory:

cd data/mini-MIAS/
mkdir images_original
mkdir images_processed
  • Move to the images_original directory and download the raw un-processed images:
cd images_original
wget http://peipa.essex.ac.uk/pix/mias/all-mias.tar.gz
  • Unzip the dataset then delete all non-image files:
tar xvzf all-mias.tar.gz
rm -rf *.txt 
rm -rf README
  • Move back up one level and move to the images_processed directory. Create 3 new directories there (benign_cases, malignant_cases and normal_cases):
cd ../images_processed
mkdir benign_cases
mkdir malignant_cases
mkdir normal_cases
  • Now run the python script for processing the dataset and render it usable with Tensorflow and Keras:
python3 ../../../src/dataset_processing_scripts/mini-MIAS-initial-pre-processing.py

DDSM and CBIS-DDSM datasets

These datasets are very large (exceeding 160GB) and more complex than the mini-MIAS dataset to use. Downloading and pre-processing them will therefore not be covered by this README.

Our generated CSV files to use these datasets can be found in the /data/CBIS-DDSM directory, but the mammograms will have to be downloaded separately. The DDSM dataset can be downloaded here, while the CBIS-DDSM dataset can be downloaded here.

Authors

  • Adam Jaamour
  • Ashay Patel
  • Shuen-Jen Chen