MSF-Model: Multi-Scale Feature Fusion-Based Domain Adaptive Model for Breast Cancer Classification of Histopathology Images

One of the most common causes of mortality for women globally is breast cancer. Early breast cancer identification could make it possible for people to receive the appropriate treatment to save their lives and return to their routine lives. Breast cancer diagnosis by histopathology is referred to as the gold standard. In recent years, convolutional neural network-based techniques are used for breast cancer classification. However, they faced domain adaptation, small objects retention, and feature extraction issues of complex microscopic images. In this study, we introduced multi-scale feature fusion-based domain adaptive model for breast cancer classification using histopathology images. It has two blocks and six lightweight sub-models where each block contains three models. Dilated layers are used in sub-models to overcome the disappearing of small objects in deep layers. Reducing the disappearing of small objects helped to extract better features for higher performance. Multiple heterogeneous feature extractors are used in this study which helped to extract various features. Extracted features are fused and reduced by retaining better features. Learning of model from natural images to complex microscopic images has limitation of domain adaptation. Same domain transfer learning is used in this study to overcome the limitations of different domain transfer learning. Model is trained on patchcamelyon17 dataset and weights of this training are further used for same domain transfer learning. Pre-trained weights are further used for the training of proposed model on BreaKHis dataset. A number of conventional data augmentation techniques are used as complex models require higher number of samples for the tuning of weights. Local window based CLAHE contrast enhancement technique is used to increase foreground-background contrast and remove noise. The proposed model achieved 98.00% precision, 98.15% recall, 98.08% f-measure, and 98.23% accuracy on test data. To best of our knowledge, it surpassed state-of-the-art models.