Deep convolutional network for breast cancer classification: enhanced loss function (ELF)

  • Smarika Acharya
  • Abeer AlsadoonEmail author
  • P. W. C. Prasad
  • Salma Abdullah
  • Anand Deva
Part of the following topical collections:
  1. Deep Learning, Parallel Computing in Biomed Sciences & Healthcare


The accurate classification of the histopathological images of breast cancer diagnosis may face a huge challenge due to the complexity of the pathologist images. Currently, computer-aided diagnosis is implemented to get sound and error-less diagnosis of this lethal disease. However, the classification accuracy and processing time can be further improved. This study was designed to control diagnosis error via enhancing image accuracy and reducing processing time by applying several algorithms such as deep learning, K-means, autoencoder in clustering and enhanced loss function (ELF) in classification. Histopathological images were obtained from five datasets and pre-processed by using stain normalisation and linear transformation filter. These images were patched in sizes of 512 × 512 and 128 × 128 and extracted to preserve the tissue and cell levels to have important information of these images. The patches were further pre-trained by ResNet50-128 and ResNet512. Meanwhile, the 128 × 128 were clustered and autoencoder was employed with K-means which used latent feature of image to obtain better clustering result. Classification algorithm is used in current proposed system to ELF. This was achieved by combining SVM loss function and optimisation problem. The current study has shown that the deep learning algorithm has increased the accuracy of breast cancer classification up to 97% compared to state-of-the-art model which gave a percentage of 95%, and the time was decreased to vary from 30 to 40 s. Also, this work has enhanced system performance via improving clustering by employing K-means with autoencoder for the nonlinear transformation of histopathological image.


Convolutional neural network Breast cancer K-means Autoencoder Support vector machine 



There was no grant support for this study.

Compliance with ethical standards

Conflict of interest

The authors indicate full freedom of investigation and no potential conflict of interest.

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Not required.


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© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Computing and MathematicsCharles Strut UniversitySydneyAustralia
  2. 2.Department of Computer EngineeringUniversity of TechnologyBaghdadIraq
  3. 3.Faculty of Medicine and Health SciencesMacquarie UniversitySydneyAustralia

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