Medical image classification via quantum neural networks [article]

Natansh Mathur, Jonas Landman, Yun Yvonna Li, Martin Strahm, Skander Kazdaghli, Anupam Prakash, Iordanis Kerenidis
<span title="2021-09-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification. In recent years, quantum machine learning techniques have been put forward as a way to potentially enhance performance in machine learning applications, both through quantum algorithms for linear algebra and quantum neural networks. In this work, we study two different quantum neural network techniques for medical image classification: first by employing
more &raquo; ... um circuits in training of classical neural networks, and second, by designing and training quantum orthogonal neural networks. We benchmark our techniques on two different imaging modalities, retinal color fundus images and chest X-rays. The results show the promises of such techniques and the limitations of current quantum hardware.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:2109.01831v1</a> <a target="_blank" rel="external noopener" href="">fatcat:uobfchpv5bagtauaofyt6lhlku</a> </span>
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