Automatic Classification of Medical Image Modality Using Quantum Convolutional Neural Network

Abstract

Medical imaging plays an important role in the early diagnosis of various diseases. In machine learning algorithms, texture, color, and shape features are primarily used. This may result in a system with inadequate model generalization capabilities. The development of an end-to-end model that can classify unprocessed medical images is now possible by recent advances in deep learning. The heavy demands of deep learning on memory and computer resources result in further advancement in the processing of complex data. As a result, quantum computing offers potential solutions that take advantage of quantum mechanics concepts like entanglement, superposition, and interference. This chapter introduces a new medical image classification model based on the quantum convolutional neural network (QCNN). Three benchmark datasets are used to evaluate the performance of QCNN; PneumoniaMNIST, VesselMNIST3D, and brain tumor MRI images. The experimental results showed that the proposed model based on the proposed QCNN is very promising.

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