Utilizing Quantum Variational Circuits to Perform Quantum Convolutions in Hybrid "Quanvolutional" Neural Networks for Skin Cancer Detection

Cancer is one of the leading causes of death worldwide. Mutations in humans can alter oncogenes or tumor suppressor genes, leading to uncontrolled cell growth. The most common form of cancer is skin cancer which if caught early, is much easier to treat, highlighting the importance of a sophisticated skin cancer detection system. In this study, we propose a novel method to detect skin cancer early, utilizing quantum variational circuits to perform a quantum convolution as a pre-processing step on image data for classical Convolutional Neural Networks (CNN) to create a hybrid "Quanvolutional" Neural Network. Utilizing a dataset of over 8,100 images which included images of other skin conditions including Herpes Simplex, Burns, Xerosis, folliculitis, and more, our model obtained a validation accuracy of 99.30% for the classification of Skin Cancer. This performance surpasses that of similar, purely classical CNNs, despite the presence of noise from other skin conditions. Our study not only demonstrates significant potential for skin cancer detection, but also the efficiency and effectiveness of quantum pre-processing layers in hybrid Quanvolutional Neural Networks. Future work can utilize our Quantum Machine Learning methods, leading to further breakthroughs within the medical field and quantum computing as a whole.