convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer
This work addresses the need for a computational model to assist in diagnosing HER2 breast cancer, potentially reducing reliance on expensive medical tests, but it appears incremental as it applies existing CNN methods to a specific medical dataset.
The study tackled the problem of detecting HER2 breast cancer, a more aggressive type, by developing a deep neural network called convoHER2 that classifies cancer stages from medical images, achieving accuracies of 85% on H&E images and 88% on IHC images.
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.