Our solution for ICIAR 2018 Grand Challenge dataset on BreAst Cancer Histology images

In the present work, we have proposed an approach for breast cancer image classification,implemented using Tensorflow and Keras, which at first uses five fine-tuned, pre-trained deep learning models for classification breast cancer histology im-ages. Then a fuzzy ensemble approach is introduced where the confidencescores of the five models are fused using Choquet integral, Coalition game theory and Information theory. The dataset used for evaluating the proposed model is the ICIAR 2018 Grand Challenge on Breast Cancer Histology (popularly known as BACH) images. We have considered both 2-class (Malignant and Benign) and 4-class (Benign, In-situ carcinoma,Invasive carcinoma, and Normal tissue). To the best of our knowledge,our experimental results outperform many state-of-the-art methods.

• Team Members
• Reference Paper
• Method Overview
• Dataset
• Results
• Dependencies
• Contact

• Subhankar Sen LinkedIn
• Pratik Bhowal LinkedIn
• Prof. Juan D. Velasquez Silva,University of Chile LinkedIn ,Google Scholar
• Associate Prof. Ram Sarkar,Jadavpur University,Kolkata LinkedIn , Google Scholar

If you find this work useful for your publications, please consider citing:

@article{bhowal2021fuzzy,
  title={Fuzzy ensemble of deep learning models using choquet fuzzy integral, coalition game and information theory for breast cancer histology classification},
  author={Bhowal, Pratik and Sen, Subhankar and Silva, Juan D Velasquez and Sarkar, Ram},
  journal={Expert Systems with Applications},
  pages={116167},
  year={2021},
  publisher={Elsevier}
}

Click to access the BACH dataset

• Python3
• Tensorflow
• Keras
• OpenCV

In case of doubt or further collaboration, feel free to email us ! 😊

• Subhankar Sen ([email protected])
• Pratik Bhowal ([email protected])