A Breast Cancer Classification Technique Based On Histological Images Using Convolutional Neural Networks

Abstract

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. The diagnosis is based on the qualification of histopathologist, who will look for abnormal cells. However, if the histopathologist is not well-trained, this may lead to wrong diagnosis. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures.

Convolutional Neural Networks for Binary class classification and multiclass classification. The Binary class classification is used to classify the cancer cells to malignant and benign. And the Multiclass classification these classes into different subclasses like adenosis, fibroadenoma, phyllodes tumour, tabular adenoma for benign class and ductal carcinoma, lobular carcinoma, mucinous carcinoma, papillary carcinoma for malignant class. The result will show Convolutional Neural Networks outperformed the handcrafted feature based classification with high accuracy in both binary and multiclass classification.