Breast cancer image classification using deep learning augmented with attention mechanism

The use of the medical imaging approach for breast cancer diagnosis has become increasingly reliable with the advent of deep learning techniques. Advances in deep learning have strengthened the reliability of medical imaging-based diagnosis. However, many existing models struggle to generalize across diverse imaging modalities and heterogeneous datasets. The study essentially focuses on addressing a key limitation of deep learning in the context of its limited ability to generalize effectively across diverse breast cancer datasets and image formats. This study showcases a custom deep learning model combining a dilated Convolutional Neural Network (CNN) infused with an attention mechanism using model infusion techniques for classifying breast cancer images. The model was trained on a dataset comprising various imaging modalities, including histopathology, ultrasound, Magnetic Resonance Imaging (MRI), and mammograms, with experimental evaluation showing that the model achieves high overall performance, with accuracy, precision, recall, and AUC all approaching optimal values. The confusion matrix analysis revealed low false positives (11 benign misclassified as malignant) and false negatives (19 malignant misclassified as benign), underscoring the model’s reliability in medical applications where diagnostic accuracy is critical. The model was also tested on unseen data, showing robust generalization across different imaging modalities. Although the model performance showcases a promising outcome, limitations such as dataset diversity and the reliance on image-based diagnosis are acknowledged. Further testing and validation in clinical settings, as well as the integration of multi-modal data, are suggested to enhance the model’s performance and applicability in real-world breast cancer diagnosis. Overall, this study indicates the potential of advanced CNN architectures in improving breast cancer detection and classification.