Breast cancer detection using mammography images, utilizing deep learning models

Supported datasets:

• InBreast
• CBIS-DDSM (Curated Breast Imaging Subset of DDSM)
• MIAS (Mammography Image Analysis Society)

Supported models:

• Generally supported models
  • YOLO
  • Any model that supports YOLO / COCO style dataset
• Customized UaNet for 2D mammography images

1. Clone this repository

git clone https://github.com/monajemi-arman/breast_cancer_detection

2. Install prerequisites

cd breast_cancer_detection
pip install -r requirements.txt

2. Download the following datasets
https://www.kaggle.com/datasets/ramanathansp20/inbreast-dataset
https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=22516629
https://www.kaggle.com/datasets/kmader/mias-mammography

3. Move dataset files
First create 'datasets' directory:

mkdir datasets/

Then, extract and move the files to this directory so as to have the following inside datasets/:

• INbreast Release 1.0/
• CBIS-DDSM/
• all-mias/

4. Convert datasets to YOLO (and COCO) format

python convert_dataset.py

After completion, images/, labels/, dataset.yaml, annotations.json would be present in the working directory.

• Install Ultralytics

pip install ultralytics

• Train your desired YOLO model

yolo train data=dataset.yaml model=yolov8n

Example of prediction using YOLO ultralytics framework:

yolo predict model=runs/detect/train/weights/best.pt source=images/cb_1.jpg conf=0.1 

• Install prerequisites

pip install detectron2

The purpose of detectron.py is to train and evaluate a Faster R-CNN model using detectron2 platform.

python detectron.py

• Clone UaNet repository

# Make sure you cd to breast_cancer_detection first
# cd breast_cancer_detection
git clone https://github.com/uci-cbcl/UaNet

• Apply patch

python patches_apply.py
# patch python module is in requirements.txt
# if not installed yet, do `pip install patch`

• Prepare dataset

# Convert datasets to images/ masks/
python convert_dataset.py -m mask
# Convert to 3D NRRD files
python to_3d_nrrd.py

• Move dataset to model directory

# While in breast_cancer_detection directory
mv UaNet-dataset/* UaNet/data/preprocessed/
# Remove old default configs of UaNet
rm UaNet/src/split/*
mv split/* UaNet/src/split/

• Start training

cd UaNet/src
python train.py