convoHER2: A Deep Neural Network for MultiStage Classification of HER2 Breast Cancer

Abstract

Generally, human epidermal growth factor 2
(HER2) breast cancer is more aggressive than other kinds of
breast cancer. Currently, HER2 breast cancer is detected using
expensive medical tests are most expensive. Therefore, the aim of
this study was to develop a computational model named
convoHER2 for detecting HER2 breast cancer with image data
using convolution neural network (CNN). Hematoxylin and eosin
(H&E) and immunohistochemical (IHC) stained images has been
used as raw data from the Bayesian information criterion (BIC)
benchmark dataset. This dataset consists of 4873 images of H&E
and IHC. Among all images of the dataset, 3896 and 977 images
are applied to train and test the convoHER2 model, respectively.
As all the images are in high resolution, we resize them so that we
can feed them in our convoHER2 model. The cancerous samples
images are classified into four classes based on the stage of the
cancer (0+, 1+, 2+, 3+). The convoHER2 model is able to detect
HER2 cancer and its grade with accuracy 85% and 88% using
H&E images and IHC images, respectively. The outcomes of this
study determined that the HER2 cancer detecting rates of the
convoHER2 model are much enough to provide better diagnosis
to the patient for recovering their HER2 breast cancer in future.