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.
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 output
and 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 eithermini-MIAS
orCBIS-DDMS
.MODEL
is the model to use. Must be eitherbasic
oradvanced
.RUNMODE
is the mode to run in (train
ortest
). Default value istrain
.IMAGESIZE
is the image size to feed into the CNN model (small
- 512x512px; orlarge
- 2048x2048px). Default value issmall
.-v
is a flag controlling verbose mode, which prints additional statements for debugging purposes.
-
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
andimages_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
andnormal_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
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.
- Adam Jaamour
- Ashay Patel
- Shuen-Jen Chen