Breast cancer histopathological images classification using deep learning

Author(s): Pokala PranayKumar, Raul Villamarin Rodriguez, S. Phanikumar


The examination of breast cancer by investigating histopathological images personate a serious role in the patient’s development and deep learning tactics is used to get a set of parameters from images used to build deep convolutional networks. We adapted the shufflenet model from pre-trained models for the multi-class classification of histopathological images of breast cancer by using the transfer learning technique. All our results are used to show that transfer learning provides the finest examination of breast cancer images. The average accuracy of all classes of breast tumor cells is 95±% using MATLAB. In this paper, we have proposed our methods of using pretrained networks in the transfer learning process using MATLAB on histopathological images of breast cancer. All results shown in this paper are on the augmented dataset in multi-class classification. Future researchers could use different networks with other training options and different parameters.

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