An improved hybrid quantum-classical convolutional neural network for multi-class brain tumor MRI classification

The efficiency of quantum computing has recently been extended to machine learning, which has made a significant impact on quantum machine learning. The hybrid structure of quantum and classical ones has developed into the most successful application mode currently due to noisy intermediate scale quantum limitations. In this paper, an improved hybrid quantum-classic convolutional neural network (HQC-CNN) with fast training speed, lightweight, and high performance is proposed. Its convolution layer realizes feature mapping through parameterized quantum circuit, while other layers keep classic operation and finally complete the task of four classifications of brain tumors. The experiment in this paper is based on kaggle brain tumor magnetic resonance imaging public dataset. The final experimental results show that HQC-CNN can effectively classify meningioma, glioma, pituitary, and no tumor with a classification accuracy of 97.8%. When compared to numerous well-known landmark models, HQC-CNN has obvious advantages.