Efficient breast cancer diagnosis using multi‐level progressive feature aggregation based deep transfer learning system

Abstract Breast cancer is a worldwide fatal disease that exists mostly among women. The deep learning technique has proven its effectiveness, but the performance of the existing deep learning systems is quite compromising. In this work, a deep transfer learning system is suggested for efficient breast cancer classification from histopathology images. This system is based on a novel multi‐level progressive feature aggregation (MPFA) and a spatial domain learning approach. The combination of a pretrained Resnet101 backbone network with MPFA is implemented to extract more significant features. In addition, a mixed‐dilated spatial domain learning network (MSLN) is further incorporated to enhance the receptive field and increase discrimination between features. The proposed method achieved superior performance as compared to the existing state‐of‐the‐art methods, offering 99.24% accuracy, a 98.79% F‐1 score, 98.59% precision, and 98.99% recall values over BreaKHis dataset. An ablation study is carried out over the ICIAR2018 dataset to verify the generalizability and effectiveness of the system.