FCCS-Net: Breast cancer classification using Multi-Level fully Convolutional-Channel and spatial attention-based transfer learning approach

Breast cancer ranks among one of the most lethal cancer varieties among the many types that exist. Timely detection is paramount, as late diagnosis can exacerbate its severity. Computer-aided detection systems can complement the clinician in early decision-making. Therefore, in this study, a multi-level, complete convolution-driven attention-based transfer learning approach named 'FCCS-Net' has been proposed, for breast cancer classification. In contrast to the shared multi-layer perceptron (MLP)-based attention mechanism, the proposed approach employs a fully convolutional attention mechanism to focus the important cellular features in inter-channel and intra-channel feature space. This proposed attention is applied across multiple levels of a pre-trained ResNet18 model, supplemented with additional residual connections. The performance of the proposed FCCS-Net is tested on publicly available datasets such as 'BreakHis','IDC' and 'BACH', containing breast cancer histopathology images. On the BreakHis dataset, the proposed method achieves accuracy rates of 99.25%, 98.32%, 99.50%, and 96.98% at 40X, 100X, 200X, and 400X optical zoom levels, respectively. In the case of the IDC dataset, a classification accuracy of 90.58% is attained at 40X magnifications, whereas with BACH dataset 91.25% average classification accuracy has been obtained. These findings establish the robustness and efficacy of the FCCS-Net in detecting breast cancer through histopathology images. The area focused by each attention layer has also been visually explained. The integration of multi-level, fully convolutional attention with supplementary residual connections holds the potential to advance breast cancer detection methodologies. The relevant PyTorch code for implementing the FCCS-Net model can be accessed at https://github.com/maurya123ritesh47/FCCS-Net.