Hybrid Quantum-Classical Neural Network for Breast Cancer Detection

Breast cancer is among the leading causes of mortality among women worldwide, emphasizing the urgent need for accurate and efficient diagnostic tools. Machine learning-based systems have been heavily investigated in this regard. Quantum computing has garnered considerable attention recently; however, few studies have focused on its application to this specific problem. In this work, we integrate quantum computing principles with classical neural networks (QCNNs) to enhance the detection accuracy and computational efficiency of breast cancer diagnosis using the MedMNIST dataset. The hybrid QCNN framework leverages the power of quantum computing to perform complex feature mapping and extraction while reducing the estimated circuit depth of the quantum circuit compared to pure quantum neural network schemes. The proposed model achieved an accuracy of over 84%, a sensitivity of 82%, and a specificity of 86.0%, outperforming the state-of-the-art model developed for the same purpose. The focus of the study is to improve the accuracy and generalizability of classifying complex mammograms, as opposed to current quantum machine learning models, for practical applications in healthcare. We believe this study will support advancing the prospect of AI-enabled medical diagnosis with state-of-the-art quantum computing principles.