convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of cancer. Currently, HER2 detected using variety medical test which are most expensive. Therefore, the aim this study was to develop a computational model named convoHER2 for detecting with image data convolution neural network (CNN). Hematoxylin and eosin (H&E) immunohistochemical (IHC) stained images were used as raw from Bayesian information criterion (BIC) benchmark dataset. This dataset consists 4873 H&E IHC. Among all dataset, 3896 977 applied train model, respectively. All resized due high resolution forming better detection performance model. Moreover, classified into four different labels (0+, 1+, 2+, 3+) identifying grade The able detect its accuracy 85% 88% IHC images, outcomes determined that rates much enough provide diagnosis patient recovering their in future.