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

Abstract

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.

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Correspondence to Abeer Alsadoon.

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Acharya, S., Alsadoon, A., Prasad, P.W.C. et al. Deep convolutional network for breast cancer classification: enhanced loss function (ELF). J Supercomput 76, 8548–8565 (2020). https://doi.org/10.1007/s11227-020-03157-6

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Keywords

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