Automatic Magnification Independent Classification of Breast Cancer Tissue in Histological Images Using Deep Convolutional Neural Network

  • ShalluEmail author
  • Rajesh Mehra
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


This paper proposes a new model for automatic classification of breast cancer tissues images using convolution neural network on BreakHis dataset. The main characteristic of the proposed model is its independency on the magnification factors of the images. The presence of pooling layer only in the last convolutional layer is the beauty of this model, which assists in the prevention of information loss. Data augmentation technique was used to increase the size of the dataset as convolution neural network relies on the size of the dataset for its better performance. For model evaluation, the classification performance of the proposed model was compared with the recent work and found that the proposed model outperforms the existing one with an average accuracy of 85.3% as well as robust to the images with different magnification factor. Employment of additional data, deeper architecture and consideration of factors like filter size, pooling strategy, optimiser, loss function can be the future possibilities for this work.


Breast cancer Histopathology Convolutional neural networks Magnification factor Computer-aided diagnosis 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of Technical Teachers Training and ResearchChandigarhIndia

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