An efficient hardware implementation of Convolutional Neural Network in detect Breast Cancer Histopathology Image

Abstract

This paper presents our work on evaluating the effectiveness of a novel deep convolutional neural network architecture (CNN) for classifying breast histology images for cancer risk factors as negative or positive. Also, the hardware structure of the proposed model was successfully synthesized and verified. The results indicate that a CNN trained on a small dataset achieved an overall AUC (Area under ROC Curve - ROC is an acronym for receiver operating characteristic) value of 0.922 across a set of 55505 test images. In addition, the time it takes to classify each image is within 3.8 milliseconds instead of a task that even trained pathologists take hours to complete.