HIC-net: A deep convolutional neural network model for classification of histopathological breast images

Abstract

In this study, a convolutional neural network (CNN) model is presented to automatically identify cancerous areas on whole-slide histopathological images (WSI). The proposed WSI classification network (HIC-net) architecture performs window-based classification by dividing the WSI into a certain plane. In our method, an effective pre-processing step has been added for WSI for better predictability of image parts and faster training. A large dataset containing 30,656 images is used for the evaluation of the HIC-net algorithm. Of these images, 23,040 are used for training, 2560 are used for validation and 5056 are used for testing. HIC-net has more successful results than other state-of-art CNN algorithms with AUC score of 97.7%. If we evaluate the classification results of HIC-net using softmax function, HIC-net success rates have 96.71% sensitivity, 95.7% specificity, 96.21% accuracy, and are more successful than other state-of-the-art techniques which are used in cancer research. (C) 2019 Elsevier Ltd. All rights reserved.