Comparison of CNN and QCNN Performance in Binary Classification of Breast Cancer Histopathological Images

Breast cancer is a leading cause of mortality among women, with early detection being crucial for improving outcomes. This study explores the use of Quantum Convolutional Neural Network (QCNN) for automating breast cancer detection through histopathological images. We compare QCNNs with classical Convolutional Neural Network (CNN) for binary classification of benign and malignant tumors using the BreakHis dataset. Our results demonstrate that QCNN outperform CNN in accuracy, especially in smaller batch sizes, and offer faster convergence with improved computational efficiency. This highlights QCNNs’ potential in enhancing diagnostic precision and efficiency in medical imaging.