Brain Tumor Recognition based on Classical to Quantum Transfer Learning

We expand the idea of transfer learning, generally applied in current machine learning paradigm, to the evolving hybrid neural network contrived out of traditional and quantum components for brain tumor recognition from MRI images. The proposed model is a perfect synergy of traditional classical component and a revolutionary quantum component. The notion of traditional components is to customize a pre-trained network to extract features from brain tumor MRI images, whereas the notion of the quantum components is to employ variational quantum circuit to act as a classifier with learnable parameters. Exhaustive simulation experiments reveals the efficacy of the quantum transfer learning scheme to beat the performance of the conventional classical transfer learning.