Breast_Cancer_Detection

Breast cancer detection using mammography images, utilizing deep learning models

Prerequisites

• Nvidia CUDA drivers
  • Install a PyTorch compatible version of CUDA from:
    • Your Linux repository

    apt install nvidia-cuda-toolkit
    

    • NVIDIA website for Windows and Linux
      • Link to download page
• Pytorch with CUDA support
  • Visit PyTorch website for more information

These two must be installed manually or else will break installation of other requirements later on.

Datasets

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

Download

Google Colab

• Use download_datasets_colab.ipynb jupyter notebook in Google Colab to download all datasets.
• You will need to upload your 'kaggle.json' when the notebook gives you an upload dialog.
• After logging in to kaggle, you can get your kaggle json in API section of https://www.kaggle.com/settings.
• The notebook will clone this repository and download all datasets.

Manual

Dataset links:

• https://www.kaggle.com/datasets/ramanathansp20/inbreast-dataset
• https://www.kaggle.com/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset
• https://www.kaggle.com/datasets/kmader/mias-mammography

Download the above datasets and after cloning this repository, create the following directories:

• breast_cancer_detection/
  • datasets/
    • all-mias/
      • mdb001.pgm
      • ...
    • CBIS-DDSM/
      • csv/
      • jpeg/
    • INbreast Release 1.0/
      • AllDICOMs/
      • ...

Copy datasets to directories accordingly.

Visualizer

After converting the datasets to COCO / YOLO style in the next section (Usage), you may visualize the standardized dataset using the following methods.

COCO Style dataset

python visualizer.py -m coco -d train/images -l train.json 

YOLO Style dataset

python visualizer.py -m yolo -d train/images -l train/labels 

Usage

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.

---

YOLO

Training

• Install Ultralytics

pip install ultralytics

• Train your desired YOLO model

yolo train data=dataset.yaml model=yolov8n

Prediction

Example of prediction using YOLO ultralytics framework:

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

---

Faster R-CNN (Using Detectron2)

• Install prerequisites

pip install detectron2

Train

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

python detectron.py

---

UaNet

Training

• Clone UaNet repository (patched)

# Make sure you cd to breast_cancer_detection first
# cd breast_cancer_detection
git clone https://github.com/monajemi-arman/UaNet_2D

• 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_2D/data/preprocessed/
# Remove old default configs of UaNet
mv split/* UaNet_2D/src/split/

• Start training

cd UaNet_2D/src
python train.py