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

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