Brain Tumor Classification Using Hybrid Classical-Quantum Transfer Learning

Abstract

Magnetic Resonance Imaging (MRI) images reveal unique abnormal patterns in brain tumors. These patterns play an important role in diagnosis and therapy planning. This study proposed a new model that combines Convolution Neural Network (CNN) and Parameterized Variational Quantum Circuit (VQC) to better diagnose and categorize brain tumors. The model extracted features from MRI images using pre-trained systems such as VGG16, VGG19, and ResNet-18. Among these models of CNN, the base model was selected as VGG16 for feature extraction which yield better performance. The features were subsequently reduced via an affine transformation and passed through a VQC for the hybrid model. The VQC used quantum superposition and entanglement as tools for categorization. The hybrid model performed better than base model due to the representation of feature in large space called Hilbert space. Using n qubit of quantum, 2ⁿ states were represented in the Hilbert space. Using VQC, the complex high dimensional relationship of features was learnt and also the performance of the hybrid model was optimized by integrating VQC to VGG16. The experiment was done integrating the pennylane simulator with pytorch.