Organ classification Using Quantum Convolution Network
Prashant Gohel, Amit K. Chakraborty, Kameshwar Rao JV · 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) · 2022
Automated body part recognition is an essential part of medical image analysis. It is one of the core aspects of any medical image identification or segmentation. It reduces search time with increased accuracy in detecting an organ of interest. Here, we present a quantum machine learning based organ classification model for X-RAY data sets of hand, ankle, knee, and chest. We have used deep quanvolutional neural network. Quanvolutional neural network is a hybrid neural network where input data are encoded into quantum encoding, and then it is processed with a classical neural network. It is also called HQCNN (Hybrid Quantum Convolutional Neural Net-work) [3]. Our results show that HQCNN based implementation gives 92.5% validation accuracy with fast convergence, while classical CNN gives 93% validation accuracy.