IHCNet: Advancing Multi-Stage HER2 Status Detection in Breast Cancer Using Interpretable Robust Transfer Learning Model from IHC Images
Md Sakib Hossain Shovon, Jungpil Shin, M. F. Mridha · 2023
Human Epidermal Growth Factor Receptor (HER2) has become one the most dangerous subtype of Breast Cancer (BC). According to the guidance of CAP/ASCO, HER2 expression can be classified between 0 and 3+ range in terms of its severity level. it can be detected from immunohistochemical (IHC) of several classes such as 0, 1+, 2+, and 3+. Early diagnosis can alleviate and help pathologists to take early treatment. Utilizing artificial intelligence (AI) in digital pathology, a robust transfer learning (TL) model has been utilized known as IHCNet based on DenseNet201 to diagnose BC from the distinct expression of HER2 subtypes. This model is introduced with a fine-tuned combination of Global Average Pooling (GAP), Batch Normalization (BN), Dense Layers with Swish activation functions, Dropout layers, and a Dense classifier with a softmax function. IHCNet exceeded all other state-of-art models with an Accuracy of 93.45%, Precision of 94.01%, and Recall of 93.14%, on the BCI dataset, maintaining promising improvement. Finally, Grad-CAM has been incorporated into the proposed model to explain the model more intuitively. Grad-CAM explains IHCNet performances in different convolution layers by generating heat maps.