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All Projects → abhinavsagar → Breast Cancer Classification

abhinavsagar / Breast Cancer Classification

Licence: mit
Breast Cancer Classification using CNN and transfer learning

Labels

jupyter-notebookdeep-learningneural-networkdeep-neural-networksconvolutional-neural-networks

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Breast-cancer-classification

Breast Cancer Classification using CNN and transfer learning

IMPORTANT

Absolutely, under NO circumstance, should one ever screen patients using computer vision software trained with this code (or any home made software for that matter).

Check out the corresponding medium blog post https://towardsdatascience.com/convolutional-neural-network-for-breast-cancer-classification-52f1213dcc9.

Data

The dataset can be downloaded from here. This is a binary classification problem. I split the data as shown-

dataset train
  benign
   b1.jpg
   b2.jpg
   //
  malignant
   m1.jpg
   m2.jpg
   //  validation
   benign
    b1.jpg
    b2.jpg
    //
   malignant
    m1.jpg
    m2.jpg
    //...

Environment and tools

  1. Jupyter Notebook
  2. Numpy
  3. Pandas
  4. Scikit-image
  5. Matplotlib
  6. Scikit-learn
  7. Keras

Installation

pip install numpy pandas scikit-image matplotlib scikit-learn keras

jupyter notebook

Model

model

Results

Loss/Accuracy vs Epoch

loss/accuracy

loss/accuracy

Confusion Matrix

roc-auc

ROC-AUC curve

roc-auc

Correct/Incorrect classification samples

results

results

The model is able to reach a validation accuracy of 98.3%, precision 0.65, recall 0.95, f1 score of 0.77 and ROC-AUC as 0.692.

References

  1. https://peerj.com/articles/6201.pdf

  2. https://arxiv.org/pdf/1811.04241

  3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440620/

Citing

If you find this code useful in your research, please consider citing the blog:

@misc{sagarconvolutional,
  Author = {Abhinav Sagar},
  Title = {Convolutional Neural Network for Breast Cancer Classification},
  Year = {2019},
  Journal = {Towards Data Science},
}

License

MIT License

Copyright (c) 2019 Abhinav Sagar

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].