Breast Cancer Histopathology Image Classification with Deep Convolutional Neural Networks

Abstract

This work addresses the problem of intra-class classification of Breast Histopathology images into Eight (8) classes of either Benign or Malignant Cell. Current manual features extraction and classification is fraught with inaccuracies leading to high rate false negatives with attendant mortality. Deep Convolutional Neural Networks (DCNN) have been shown to be effective in classification of Images. We adopted a DCNN architecture combined with Ensem ble learning method using TensorFlow Framework with Backpropagation training and ReLU activation function to achieve accurate automated classification of these Images. We achieved inter-class classification accuracy of 91.5% with the BreakHis dataset.