Enhanced Hybrid Quantum Neural Network for Breast Cancer Detection

Early diagnosis of breast cancer is crucial to combat its severity. Yet, it’s a challenging endeavor to tackle via traditional diagnosis methods. Several machine learning architectures have already been proposed to assist medical experts in diagnosis. However, architectural complexity hinders the goal of developing a cost-effective diagnosis model. Recently, quantum computing has become popular in machine learning due to its potential computational benefits. Although quantum computing has yet to show any significant improvement in machine learning due to its limited resources and complexity, it may reduce the computational complexity of machine learning in the future. Therefore, we proposed a novel hybrid classical-quantum machine learning model for breast cancer prediction. We collected the BreastMNIST dataset of 780 benign and malignant full-image mammogram samples. Our task is to create an efficient breast cancer-classifying hybrid machine learning model. The proposed method employs a modified ResNet50 as a feature extractor and a Quantum Neural Network to train on that feature and generate desired predictions. A Convolutional layer is applied as a dimension-reduction technique for features generated through ResNet50. Our architecture proved superior to other state-of-the-art hybrid methods, achieving an accuracy of 69% against 67%. An in-depth comparative analysis has been performed that shows the novelty of our research. This study paves the path for hybrid classical-quantum machine learning research in medical image classification.