A CNN–Transformer Hybrid Model for Early Breast Cancer Detection from Mammograms

Early-stage identification of breast cancer is imperative in decreasing the cancer-related mortality rate. However, the interpretation of mammograms is difficult as the contrast is low, and lesions of interests may be subtle. This study proposes a hybrid model which combines Convolutional Neural Networks (CNN) and Transformers, to provide an accurate and timely mechanism for the detection of breast cancer. The hybrid model captures the CNN’s ability to identify local features (e.g., edges and textures) and the Transformer’s ability to identify global features and context through self-attention. The model is implemented and validated on the MIAS and CBIS-DDSM datasets. The proposed method utilizes CLAHE for noise and contrast enhancement and captures features more effectively. The hybrid model also provides an improvement in Confirmed Contrast Enhancement (CCE) of 3.2 and greater entropy values when compared to baseline CNN models, ResNet and DenseNet. The results of the study indicate that the integration of CNN and Transformer, improves the reliability of the diagnosis, and supports the use of the model for the further development of a Real-time Clinical Decision Support (CDS) System for the early detection of breast cancer.