Classification of knee osteoarthritis based on quantum-to-classical transfer learning - Dong, Yumin; Che, Xuanxuan; Fu, Yanying; Liu, Hengrui; Zhang, Yang; Tu, Yong (Frontiers in Physics, 2023)
Quantum machine learning takes advantage of features such as quantum computing superposition and entanglement to enable better performance of machine learning models.In this paper, we first propose an improved hybrid quantum convolutional neural network (HQCNN) model.The HQCNN model was used to pre-train brain tumor dataset (MRI) images.Next, the Quantum Classical Transfer Learning (QCTL) approach is used to fine-tune and extract features based on pre-trained weights.A hybrid quantum convolutional network structure was used to test on the osteoarthritis of the knee dataset (OAI) and to quantitatively evaluate standard metrics to verify the robustness of the classifier.The final experimental results show that the QCTL method can effectively classify knee osteoarthritis with 98.36% classification accuracy. Quantum to classical transfer learning method improves classification accuracy by 1.08%.