The current research aims to design a deep learning model for nuclei segmentation using earlier research studies. This work employs a variety of methodologies, including pre-processing techniques on datasets, a multi-organ transfer learning method for segmentation. The various encoder-based models will be trained using a combination of the TNBC and MoNuSeg datasets but will be evaluated using the TNBC test dataset.
Project required packages
-
albumentations==1.1.0 -
imageio==2.12.0 -
matplotlib==3.5.0 -
numpy==1.21.4 -
opencv_python_headless==4.5.4.60 -
pandas==1.3.4 -
scikit_learn==1.0.2 -
scipy==1.7.3 -
seaborn==0.11.2 -
segmentation_models_pytorch==0.2.1 -
torch==1.10.0+cu113 -
tqdm==4.62.3
To train the model
git clone https://github.com/surya9teja/Breast-Cancer-Segmentation-using-Deep-Learning.git
cd Breast-Cancer-Segmentation-using-Deep-Learning
python3 data_pre_process.py
python3 main.py
# for Graphs and evaluation
python3 test.py
python3 graphs.py
Note: Before running the files it requires datasets from the drive and place them under Datasets/
Dataset link available at google drive
